A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.
System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 5/00 - Arrangements affording multiple use of the transmission path
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Systems, methods and apparatus for modifying a data collection trajectory for conveyors are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of conveyor types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a conveyor performance parameter. A response circuit may initiate an action in response to the conveyor performance parameter.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 5/00 - Arrangements affording multiple use of the transmission path
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
The systems and methods generally include a nuclear power plant unit assembled in a shipyard from a plurality of structural modules, each of the structural modules having manufactured components for use in power production when moored or fixed to a floor at least one of in and proximal to at least one of an offshore marine environment, a river environment and a coastal marine environment. The nuclear power plant unit is subdivided into at least one arrangement of structural modules that includes an electrical interface for one of transmitting electrical power generated by the nuclear unit and powering a system of the unit, a communications interface for communications internal or external to the unit, a user interface that is configured to permit a user to access a system of the unit, and a network interface for data communications to or from the unit.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
In example embodiments, a method of detecting an anomaly associated with a machine includes recording a data set associated with the machine; determining, by a first machine learning model, a label associated with the data set; determining whether the label is to be reviewed; and responsive to determining that the label is to be reviewed, subjecting the data set and the label to a review, and updating the label based on the review. Alternatively or additionally, in example embodiments, a method of presenting an analysis of a machine included in an industrial facility includes generating a digital twin of the machine; determining at least one property of the digital twin based on a simulation of an operation of the machine; and generating a presentation of the industrial facility that includes a visualization of the digital twin and a visual indicator of the at least one property of the digital twin.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
8.
METHODS AND SYSTEMS FOR DATA DETECTION AND DISPLAY IN AN INDUSTRIAL ENVIRONMENT WITH INTERNET OF THINGS DATA COLLECTION INCLUDING AN ADAPTIVE HEAT MAP
In some embodiments, a monitoring system for an industrial environment includes a data collector structured to collect data from at least one of a plurality of sensors, an expert system configured to analyze the collected data and generate a corresponding heat map, and a heat map interface to provide the heat map to an AR/VR device, wherein the heat map overlays a view of the underlying sensors, and wherein the data collector is further configured to collect user data, representative of a behavior of the user, from the AR/VR device.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 5/00 - Arrangements affording multiple use of the transmission path
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
A method for data communication between a first node and a second node over a data path includes estimating a rate at which loss events occur, where a loss event is either an unsuccessful delivery of a single packet to the second data node or an unsuccessful delivery of a plurality of consecutively transmitted packets to the second data node, and sending redundancy messages at the estimate rate at which loss events occur.
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH EXPERT SYSTEMS TO PREDICT FAILURES AND SYSTEM STATE FOR SLOW ROTATING COMPONENTS
Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points related to machine components, wherein at least one of the plurality of input channels is connected to a data collection point on a rotating machine component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and an expert system analysis circuit structured to analyze the collected data, wherein the expert system analysis circuit determines a failure state for the rotating machine component based on analysis of the plurality of detection values, wherein upon determining the failure state the expert system analysis circuit provides the failure state to a data storage.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
H04L 1/1867 - Arrangements specially adapted for the transmitter end
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H04B 17/23 - Indication means, e.g. displays, alarms or audible means
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
User interfaces configured to provide a list of conditions of interest to at least one entity associated with a role type stored within a role taxonomy, and to select a condition of interest in response to a user selection from the list of conditions; and a controller configured to determine a reduced dimensionality view of the data in response to a determined structure in the data and further in response to the selected condition of interest. The reduced dimensionality view a plurality of graphical elements representing mechanical portions of a machine of the industrial environment associated with the condition of interest. The reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data. The user interface is further configured to display the reduced dimensionality view.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
17.
METHODS AND SYSTEMS FOR A DATA MARKETPLACE IN A FLUID CONVEYANCE DEVICE ENVIRONMENT
Methods and systems for a data marketplace in a fluid conveyance device includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding fluid conveyance device in an industrial environment, wherein the at least one data collector is structured to collect detection values from the fluid conveyance device; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H04B 17/23 - Indication means, e.g. displays, alarms or audible means
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
A system and method for data collection and frequency analysis with self-organization functionality includes analyzing with a processor a plurality of sensor inputs, sampling with the processor data received from at least one of the plurality of sensor inputs at a first frequency, and self-organizing with the processor a selection operation of the plurality of sensor inputs.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
Systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process generally including a data circuit for analyzing a plurality of sensor inputs; and a network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system. The information sent and received by the network control system is based at least in part on a role-based reporting rule and encoded with role-based information that is used by the external system to report the information sent and received to a specified role taxonomy, the system provides sensor data to one or more other systems for data collection, and the data circuit dynamically reconfigures a route by which the system sends the sensor data based on how many devices are requesting the information.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
20.
INDUSTRIAL DIGITAL TWIN SYSTEMS USING STATE VALUE TO ADJUST INDUSTRIAL PRODUCTION PROCESSES AND DETERMINE RELEVANCE WITH ROLE TAXONOMY
Methods generally including interpreting at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state; analyzing a subset of the plurality of detection values and the state value, using at least one of a neural net or an expert system, and providing an adjustment recommendation for the industrial production process, the adjustment recommendation, at least in part, in response to a sensitivity of at least one of the plurality of input channels relative to the state value; adjusting the industrial production process in response to the adjustment recommendation; determining a relevance of the adjustment recommendation to at least one role type stored within a role taxonomy; and reporting the adjustment to the industrial production process to at least one entity associated with the role type stored within the role taxonomy.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
21.
USER INTERFACE FOR INDUSTRIAL DIGITAL TWIN SYSTEM ANALYZING DATA TO DETERMINE STRUCTURES WITH VISUALIZATION OF THOSE STRUCTURES WITH REDUCED DIMENSIONALITY
Methods generally including determining a structure in the data; by the controller, determining a relevance of the determined structure in the data to at least one role type stored within a role taxonomy; by the controller, determining a reduced dimensionality view of the data in response to the determined structure in the data. The reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors. The reduced dimensionality view further comprises a graphical element representing at least one of: mechanical portions of a machine of the industrial environment, or a sensor from the plurality of input sensors that provided data; and providing the reduced dimensionality view to a user interface that is associated with at least one entity associated with the at least one role type stored within the role taxonomy.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
22.
QUANTUM, BIOLOGICAL, COMPUTER VISION, AND NEURAL NETWORK SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS
Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
23.
INDUSTRIAL DIGITAL TWIN SYSTEMS PROVIDING NEURAL NET-BASED ADJUSTMENT RECOMMENDATION WITH DATA RELEVANT TO ROLE TAXONOMY
Data storage structured to store a plurality of detection values relating to aspects of an industrial production process and data relating to at least one role type stored within a role taxonomy; a data analysis circuit structured to interpret at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state; an optimization circuit structured to analyze a subset of the plurality of detection values and the state value using at least one of a neural net or an expert system to determine a signal effectiveness of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the signal effectiveness; and an analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
24.
DATA COLLECTION IN INDUSTRIAL ENVIRONMENT USING MACHINE LEARNING TO FORECAST FUTURE STATES OF INDUSTRIAL ENVIRONMENT BASED ON NOISE VALUES
Method for data collection in an industrial environment generally including receiving, at a switch, data from one or more variable groups of sensor inputs; monitoring the data from the one or more variable groups of sensor inputs; adaptively scheduling data collection at the switch; determining one or more noise values including one of an ambient noise, a local noise, or a vibration noise; using machine learning to forecast a future state of the industrial environment based at least in part on the determined one or more noise values; and reporting the forecasted future state of the industrial environment to an entity associated with a role type stored within a role taxonomy.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
25.
QUANTUM, BIOLOGICAL, COMPUTER VISION, AND NEURAL NETWORK SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS
Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
26.
QUANTUM, BIOLOGICAL, COMPUTER VISION, AND NEURAL NETWORK SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS
Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
27.
PLATFORM FOR FACILITATING DEVELOPMENT OF INTELLIGENCE IN INDUSTRIAL INTERNET OF THINGS WITH ADAPTIVE EDGE COMPUTE MANAGEMENT SYSTEM
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers having an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in an industrial environment; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; and an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; wherein the adaptive intelligent systems layer includes an adaptive edge compute management system that adaptively manages edge computation, storage, and processing in the IIoT system.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
H04L 1/1867 - Arrangements specially adapted for the transmitter end
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers comprising an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that provisions available computing resources within the platform; and an industrial management application platform layer that manages the platform in a common application environment.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
H04L 1/1867 - Arrangements specially adapted for the transmitter end
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Systems and methods for data collection in an industrial production system including a plurality of components are disclosed. An example system may include a sensor communication circuit structured to interpret a plurality of data values from a sensed parameter group, the sensed parameter group including a plurality of sensors including a vibration sensor and a temperature sensor, and the plurality of sensors operatively coupled to at least one of the plurality of components; a data analysis circuit structured to detect an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and a response circuit structured to modify a production-related operating parameter of the industrial production system in response to the detected operating condition.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
30.
Methods for self-organizing data collection, distribution and storage in a distribution environment
Systems for self-organizing collection and storage in a distribution environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the distribution environment, wherein the sensor inputs sense at least one of an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system selected from a group consisting of a power system, a conveyor system, a robotic transport system, a robotic handling system, a packing system, a cold storage system, a hot storage system, a refrigeration system, a vacuum system, a hauling system, a lifting system, an inspection system, and a suspension system. A system may further include a self-organizing system for: a storage operation of the data, a data collection operation, or a selection operation.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Systems for self-organizing data collection and storage in a power generation environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the power generation system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
One embodiment is directed to a system for providing wireless coverage and capacity for a public land mobile network within a building. The system comprises a pico base station comprising multiple transceiver units. The pico base station is installed in the building. The system further comprises a plurality of antennas located within the building. The plurality of antennas are located remotely from the pico base station. The pico base station is communicatively coupled to the public land mobile network. The pico base station is communicatively coupled to the plurality of antennas.
A sensor kit and associated method configured for monitoring an industrial setting is disclosed. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network, generates a data block based on the sensor data, and transmits the data block to one or more node computing devices that collectively store a distributed ledger that is comprised of a plurality of data blocks.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
40.
QUANTUM, BIOLOGICAL, COMPUTER VISION, AND NEURAL NETWORK SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS
Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.
G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/20 - Administration of product repair or maintenance
G06V 10/00 - Arrangements for image or video recognition or understanding
G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelengthActuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
G08C 15/00 - Arrangements characterised by the use of multiplexing for the transmission of a plurality of signals over a common path
H04L 43/55 - Testing of service level quality, e.g. simulating service usage
42.
Methods and systems for detection in an industrial Internet of Things data collection environment with intelligent data management for industrial processes including sensors
An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector coupled to a plurality of sensors to collect data, a data storage structured to store a plurality of data collection management plans, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data and select one of the plurality of data collection management plans, wherein the selected one of the plurality of data collection management plans is selected is at least in part based on a data analysis of received data from the plurality of sensors.
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
The system generally includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located. The local data collection system is configured to manage data collection bands.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 11/32 - Automatic controllers electric with inputs from more than one sensing elementAutomatic controllers electric with outputs to more than one correcting element
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06F 3/0488 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
H04Q 9/00 - Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
G01H 1/00 - Measuring vibrations in solids by using direct conduction to the detector
44.
System, methods and apparatus for modifying a data collection trajectory for conveyors
Systems, methods and apparatus for modifying a data collection trajectory for conveyors are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of conveyor types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a conveyor performance parameter. A response circuit may initiate an action in response to the conveyor performance parameter.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
Systems, methods and apparatus for modifying a data collection trajectory for centrifuges are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of centrifuge types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a centrifuge performance parameter. A response circuit may initiate an action in response to the centrifuge performance parameter.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04B 17/309 - Measuring or estimating channel quality parameters
G06N 5/046 - Forward inferencingProduction systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/06 - Buying, selling or leasing transactions
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
An example monitoring system for data collection includes a data collector including a plurality of sensor. The system includes a data storage to store a collector route template for the plurality of sensors with a sensor collection routine defining how the plurality of sensors. The system includes a data acquisition and analysis circuit to receive detection signals and evaluate the detection values with respect to a rule, and further, based on the evaluation of the detection values with respect to the rule, to modify the sensor collection routine.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/08 - Configuration management of networks or network elements
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04N 19/136 - Incoming video signal characteristics or properties
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
H04N 19/50 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
H04W 84/22 - Self-organising networks, e.g. ad hoc networks or sensor networks with access to wired networks
48.
SENSOR KITS AND ASSOCIATED METHODS FOR MONITORING AND MANAGING UNDERWATER INDUSTRIAL SETTINGS
A method for monitoring an underwater industrial setting using a sensor kit having a plurality of sensors and an edge device can include receiving, by an edge processing system of the edge device, reporting packets from the plurality of sensors via a self-configuring sensor kit network. Each reporting packet can include routing data and one or more instances of sensor data. The method can further include performing one or more edge operations on the sensor data and generating one or more sensor kit packets based on the edge operations. The method can include transmitting the sensor kit packets to a backend system via a public network.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
49.
Systems and methods for enabling user selection of components for data collection in an industrial environment
Systems and methods for data collection in an industrial environment are disclosed. An expert graphical user interface showing representations of components of an industrial machine to which sensors are attach is disclosed. The user interface may enable a user to select at least one of the components resulting in a search of a database of industrial machine failure modes for modes that correspond to the selected component. The corresponding failure mode may be presented to the user. The selection of the component may cause a controller to reference and implement a data collection template for configuring the system to automatically collect data from sensors associated with the selected component to detect at least one of the corresponding failure modes.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06N 5/046 - Forward inferencingProduction systems
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
A sensor kit that is configured for monitoring an industrial setting includes an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to a backend system via a public network, wherein the sensor kit packets are based on a compressed block of media content frames indicative of the sensor data.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
51.
SENSOR KITS AND ASSOCIATED METHODS FOR MONITORING AND MANAGING INDUSTRIAL SETTINGS
A method for monitoring an industrial setting using a sensor kit having a plurality of sensors and an edge device including a processing system can include receiving, by the processing system, reporting packets from one or more respective sensors of the plurality of sensors, wherein each reporting packet includes routing data and one or more instances of sensor data; generating, by the processing system, a block of media content frames, wherein each media content frame includes a plurality of frame values, each frame value being indicative of a respective instance of sensor data; compressing, by the processing system, the block of media content frames using a media codec to obtain a compressed block; generating, by the processing system, one or more server kit packets based on the compressed block; and transmitting, by the processing system, the one or more server kit packets to a backend system via a public network.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
52.
SYSTEMS AND METHODS FOR MONITORING INDUSTRIAL SETTINGS WITH SENSOR KITS
Systems and associated methods for monitoring an industrial setting using sensor kits in communication with a backend system via a communication gateway are disclosed. Each sensor kit can have a set of sensors that are registered to respective industrial settings and configured to monitor physical characteristics of the industrial settings. The communication gateway can communicate instances of sensor values from the sensor kits to a backend system. The backend system can process the instances of sensor values to monitor the industrial setting, wherein upon receiving registration data for a sensor kit to an industrial setting, the backend system automatically configures and populates a dashboard for an owner or operator of the industrial setting, wherein the dashboard provides monitoring information that is based on the instances of sensor values for the industrial setting.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
53.
SYSTEMS AND METHODS FOR MONITORING INDUSTRIAL SETTINGS WITH SENSOR KITS UTILIZING A DISTRIBUTED LEDGER
A system and associated method configured for monitoring an industrial setting is disclosed. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network, generates a data block based on the sensor data, and transmits the data block to one or more node computing devices that collectively store a distributed ledger that is comprised of a plurality of data blocks.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 41/08 - Configuration management of networks or network elements
H04W 40/02 - Communication route or path selection, e.g. power-based or shortest path routing
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
54.
INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 47/27 - Evaluation or update of window size, e.g. using information derived from acknowledged [ACK] packets
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/37 - Decoding methods or techniques, not specific to the particular type of coding provided for in groups
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 47/27 - Evaluation or update of window size, e.g. using information derived from acknowledged [ACK] packets
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/37 - Decoding methods or techniques, not specific to the particular type of coding provided for in groups
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 47/27 - Evaluation or update of window size, e.g. using information derived from acknowledged [ACK] packets
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/37 - Decoding methods or techniques, not specific to the particular type of coding provided for in groups
An industrial plant operation management platform integrating a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an industrial plant operation, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
An industrial plant operation management platform integrating a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an industrial plant operation, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
64.
INDUSTRIAL DIGITAL TWIN SYSTEMS AND METHODS WITH ECHELONS OF EXECUTIVE, ADVISORY AND OPERATIONS MESSAGING AND VISUALIZATION
An industrial plant operation management platform integrating a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an industrial plant operation, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A method for data communication between a first node and a second node over a data path includes estimating a rate at which loss events occur, where a loss event is either an unsuccessful delivery of a single packet to the second data node or an unsuccessful delivery of a plurality of consecutively transmitted packets to the second data node, and sending redundancy messages at the estimate rate at which loss events occur.
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 12/24 - Arrangements for maintenance or administration
70.
SENSOR KITS AT EDGE DEVICES FOR MONITORING AND MANAGING INDUSTRIAL SETTINGS
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H04L 12/24 - Arrangements for maintenance or administration
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
The systems and methods generally include a nuclear power plant unit assembled in a shipyard from a plurality of structural modules, each of the structural modules having manufactured components for use in power production when moored or fixed to a floor at least one of in and proximal to at least one of an offshore marine environment, a river environment and a coastal marine environment. The nuclear power plant unit is subdivided into at least one arrangement of structural modules that includes an electrical interface for one of transmitting electrical power generated by the nuclear unit and powering a system of the unit, a communications interface for communications internal or external to the unit, a user interface that is configured to permit a user to access a system of the unit, and a network interface for data communications to or from the unit.
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
G16Y 20/10 - Information sensed or collected by the things relating to the environment, e.g. temperatureInformation sensed or collected by the things relating to location
H03M 7/30 - CompressionExpansionSuppression of unnecessary data, e.g. redundancy reduction
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
82.
METHODS, SYSTEMS, KITS AND APPARATUSES FOR MONITORING AND MANAGING INDUSTRIAL SETTINGS
A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.
The present disclosure includes a method for receiving, by the processing system, reporting packets from one or more respective sensors of the plurality of sensors. Each reporting packet is sent from a respective sensor and indicates sensor data captured by the respective sensor; performing, by the processing system, one or more edge operations on one or more instances of sensor data received in the reporting packets. Generating one or more sensor kit packets based on the instances of sensor data. Each sensor kit packet includes at least one instance of sensor data. Outputting the sensor kit packets to the data handling platform. Receiving the sensor kit packets from the edge device. Generating the digital twin of said industrial setting including a digital replica of at least one industrial component of said industrial setting and being at least partially based on the sensor kit packets.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/00 - Computing arrangements based on biological models
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A system and method for data collection and frequency analysis with self-organization functionality includes analyzing with a processor a plurality of sensor inputs, sampling with the processor data received from at least one of the plurality of sensor inputs at a first frequency, and self-organizing with the processor a selection operation of the plurality of sensor inputs.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
Systems and methods for detecting operating characteristics of an industrial machine are disclosed. The detecting can include generating one or more image data sets using raw data captured by one or more data capture devices and identifying one or more values corresponding to a portion of the industrial machine within a point of interest represented by the one or more image data sets. The one or more values can be compared to corresponding predicted values and a variance data set can be generated based on the comparison of the one or more values and the corresponding predicted values. An operating characteristic of the industrial machine can be identified based on the variance data and data indicating a detection of the operating characteristic can be generated.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A method for data communication between a first node and a second node includes forming one or more redundancy messages from data messages at the first node using an error correcting code and transmitting first messages from the first node to the second node over a data path, the transmitted first messages including the data messages and the one or more redundancy messages. Second messages are received at the first node from the second node, which are indicative of: (i) a rate of arrival at the second node of the first messages, and (ii) successful and unsuccessful delivery of the first messages. A transmission rate limit and a window size are maintained according to the received second messages. Transmission of additional messages from the first node to the second node is limited according to the maintained transmission rate limit and window size.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/16 - Arrangements for detecting or preventing errors in the information received by using return channel in which the return channel carries supervisory signals, e.g. repetition request signals
H04L 12/807 - Calculation or update of the congestion window
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H03M 13/05 - Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
H03M 13/00 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes
H03M 13/37 - Decoding methods or techniques, not specific to the particular type of coding provided for in groups
METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND PREDICTED MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS
An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system detects an operating characteristic of an industrial machine, such as vibration, using one or more sensors of a mobile data collector and identify, as a condition of the industrial machine, a characteristic for the industrial machine within the knowledge base. The system can determine severity of the condition and predict and execute a maintenance action to perform against the industrial machine based on the severity of the condition.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
METHODS AND SYSTEMS FOR DATA COLLECTION AND ANALYSIS OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS AND A MOBILE DATA COLLECTOR
A system and method for causing a mobile data collector to perform a maintenance action on an industrial machine are disclosed. The mobile data collector can be deployed for detecting and monitoring vibration activity of a portion of an industrial machine. The mobile data collector can be controlled to approach a location of the industrial machine such that a vibration sensor of the mobile data collector can record a measurement of the vibration activity, which can be transmitted as vibration data to a server over a network. The server can determine a severity of the vibration activity and predict a maintenance action to perform. A signal indicative of the maintenance action can be transmitted to the mobile data collector to cause the mobile data collector to perform the maintenance action. A record of the predicted maintenance action can be stored within a ledger associated with the industrial machine.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
METHODS AND SYSTEMS FOR DATA COLLECTION AND ANALYSIS OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS AND A MOBILE DATA COLLECTOR
A system and method for causing a mobile data collector to perform a maintenance action on an industrial machine are disclosed. The mobile data collector can be deployed for detecting and monitoring vibration activity of a portion of an industrial machine. The mobile data collector can be controlled to approach a location of the industrial machine such that a vibration sensor of the mobile data collector can record a measurement of the vibration activity. The measurement of the vibration activity can be transmitted as vibration data to a server over a network, which can determine a severity of the vibration activity and predict a maintenance action to perform based on the severity of the vibration activity. A signal indicative of the maintenance action can be transmitted to the mobile data collector to cause the mobile data collector to perform the maintenance action.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
METHODS AND SYSTEMS FOR DETERMINING A NORMALIZED SEVERITY MEASURE OF AN IMPACT OF VIBRATION OF A COMPONENT OF AN INDUSTRIAL MACHINE USING THE INDUSTRIAL INTERNET OF THINGS
An industrial machine predictive maintenance system and method for determining a normalized severity measure of an impact of vibration of a component of an industrial machine. Vibration data can be captured from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine and a frequency, a peak amplitude and gravitational force of the captured vibration can be determined. A frequency range-specific segment of a multi-segment vibration frequency spectra that bounds the captured vibration based on the determined frequency can be determined, and a vibration severity level for the captured vibration data can be determined based on the determined segment and at least one of the peak amplitude and the gravitational force. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine based on the vibration severity level can be generated.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/00 - Computing arrangements based on biological models
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04B 17/309 - Measuring or estimating channel quality parameters
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
91.
Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial Internet of Things
An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
H04L 5/00 - Arrangements affording multiple use of the transmission path
92.
METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS
An industrial machine predictive maintenance method and system may include an industrial machine data analysis facility that collects data representative of conditions of portions of industrial machines received via a data collection network. Vibration data representative of a vibration of at least a portion of an industrial machine can be received from a wearable device including at least one vibration sensor used to capture the vibration data. A frequency of the captured vibration can be determined by processing the captured vibration data and, based on the frequency, a segment of a multi-segment vibration frequency spectra that bounds the captured vibration can be determined. A severity unit for the captured vibration can be calculated based on the determined segment a signal in a predictive maintenance circuit for executing a maintenance action on at least the portion of the industrial machine based on the severity unit can be generated.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/00 - Computing arrangements based on biological models
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04B 17/309 - Measuring or estimating channel quality parameters
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
93.
METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR PART IDENTIFICATION AND OPERATING CHARACTERISTICS DETERMINATION USING THE INDUSTRIAL INTERNET OF THINGS
An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may perform a method of image capture of a portion of an industrial machine in which an image capture template is provided and aligned via augmented reality with a live image in order to update a procedure for performing a service that implements a predicted maintenance action on an industrial machine. The system may perform a method of machine learning-based part recognition in which a captured image is analyzed and used to adapt a target part template, image analysis rules, or part recognition. The system may detect operating characteristics of an industrial machine via a machine learning aspect trained based on image data sets.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
Methods and systems for detecting operating characteristics of an industrial machine in which the systems include at least one data capture device configured to capture raw data of a point of interest of the industrial machine and a computer vision system. The computer vision system can generate one or more image data sets using the raw data captured, identify one or more values corresponding to a portion of the industrial machine within the point of interest represented by the one or more image data sets, compare the one or more values to corresponding predicted values, generate a variance data set based on the comparison of the one or more values and the corresponding predicted values, detect an operating characteristic of the industrial machine based on the variance data, and generate data indicating the detection of the operating characteristic.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS
An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may predict a service event from vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine signal a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
In industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may perform a method of sampling a signal at a streaming sample rate to produce a plurality of samples of the signal. Portions of the plurality of samples can be allocated to first and second signal analysis circuits based on signal analysis sampling rates less than the streaming sample rate, and the samples and the outputs of the signal analysis circuits can be stored. The system can include a sensor detecting a condition of an industrial machine to output a signal, which can be sampled at a streaming sample rate that is at least twice a dominant frequency of the signal.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Systems and methods for monitoring a vehicle steering system are disclosed. An example monitoring system for a vehicle steering system may include a vehicle steering system comprising a rack, a pinion, and a steering column; a data acquisition circuit structured to interpret a plurality of detection values corresponding input sensors operationally coupled to the rack, the pinion, or the steering column; a data storage circuit structured to store specifications, and to buffer the plurality of detection values for a predetermined length of time. The example system may further include a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a steering system analysis circuit to determine a steering system performance parameter in response to a relative phase difference and a response circuit structured to perform at least one operation in response to the steering system performance parameter.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H04B 17/23 - Indication means, e.g. displays, alarms or audible means
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Methods and systems for noise detection and removal in a motor are disclosed. An example system for monitoring a plurality of components of a motor in an industrial environment may include a data acquisition circuit to interpret a plurality of detection values, each detection value corresponding to a plurality of input sensors operationally coupled to the motor; a data processing circuit to utilize at least one of the detection values to perform at least one noise processing operation on at least a portion of the detection values; a signal evaluation circuit to determine a motor performance parameter in response to the noise processed portion of the of detection values; and a response circuit structured to perform at least one operation in response to the motor performance parameter.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H04B 17/23 - Indication means, e.g. displays, alarms or audible means
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
Methods and systems for sensor fusion in a production line environment are disclosed. An example system for data collection in an industrial production environment may include an industrial production system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the components; a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group; and a data analysis circuit to detect an operating condition of the industrial production system based at least in part on a portion of the sensor data values; and a response circuit to modify a production related operating parameter of the industrial production system in response to the detected operating condition.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06N 3/00 - Computing arrangements based on biological models
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06N 3/12 - Computing arrangements based on biological models using genetic models
Systems and methods for balancing remote oil and gas equipment are disclosed. An example system may include analog sensors coupled to a piece of equipment and an analog switch with a plurality of analog sensor channels, wherein a first analog sensor channel comprises a trigger channel coupled to a first of the analog sensors, and wherein a second one of the analog sensor channels comprises an input channel coupled to a second sensors. The analog switch may digitally derive a relative phase between the trigger channel and the input channel, utilize a PLL band-pass tracking filter to determine at least one of slow-speed RPMs or phase information for the piece of equipment, and a response circuit that provides a process change command to remotely balance at least one component of the piece of equipment based on the RPMs or the phase information.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
H02M 1/12 - Arrangements for reducing harmonics from AC input or output
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
H04L 1/1867 - Arrangements specially adapted for the transmitter end
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
B62D 5/04 - Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 17/18 - Complex mathematical operations for evaluating statistical data
patents
