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 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
2.
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.
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 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
H04W 84/18 - Réseaux auto-organisés, p. ex. réseaux ad hoc ou réseaux de détection
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.
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 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.
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. A computerized maintenance management system (CMMS) that produces orders and/or requests for service and parts responsive to the industrial machine service recommendations can be included. The system may include a service and delivery coordination facility that processes information regarding services performed on industrial machines responsive to the orders and/or requests for service and parts, thereby validating the services performed while producing a ledger of service activity and results for individual industrial machines.
G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
8.
METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS
A monitoring system for data collection in an industrial environment includes a data acquisition circuit that determines detection values received from input sensors, a multiplexor (MUX) having a number of inputs corresponding to a subset of the detection values, and a MUX control circuit that provides logical control of the MUX based on the subset of the detection values, including control of a correspondence of MUX inputs to detection values, and adaptive scheduling of select lines. The system includes a data analysis circuit that receives an output from the MUX and determines a component health status, and an analysis response circuit that responds to the component health status.
An example data collection system in an industrial environment includes a data collector in communication with a number of input channels for acquiring collected data. The system includes a data storage that stored the collected data as a number of data pools. The system includes a self-organizing data marketplace engine that receives the data pools, and that is organized based on training a marketplace self-organization with a training set, and further based on feedback from measures of marketplace success with respect to the data pools.
A monitoring apparatus, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, 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 determine an aggregate rate of data being collected from the plurality of input channels, wherein if the aggregate rate exceeds a throughput parameter of the network infrastructure, then the data analysis circuit alters the data collection to reduce the amount of data collected.
G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
G05B 19/10 - Commande à programme autre que la commande numérique, c.-à-d. dans des automatismes à séquence ou dans des automates à logique utilisant des sélecteurs
G05B 19/12 - Commande à programme autre que la commande numérique, c.-à-d. dans des automatismes à séquence ou dans des automates à logique utilisant des supports d'enregistrement
A system for data collection, processing, and utilization of signals from at least a first element in a first machine in an industrial environment generally includes a platform including a computing environment connected to a local data collection system having at least a first sensor signal and a second sensor signal obtained from at least the first machine in the industrial environment. The system includes a first sensor in the local data collection system configured to be connected to the first machine and a second sensor in the local data collection system. The system further 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 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.
An intelligent cooking system device is provided with processing, communications, and other information technology components, for remote monitoring and control and various value added features and services, embodiments of which use a renewable energy-powered electrolyser to produce hydrogen as an on-demand fuel stream for a heating element of the cooking system.
B60L 11/18 - utilisant de l'énergie fournie par des piles primaires, des piles secondaires ou des piles à combustibles
C01B 3/00 - HydrogèneMélanges gazeux contenant de l'hydrogèneSéparation de l'hydrogène à partir de mélanges en contenantPurification de l'hydrogène
C25B 1/04 - Hydrogène ou oxygène par électrolyse de l'eau
C25B 15/02 - Commande ou régulation des opérations
H01M 8/0656 - Combinaison d’éléments à combustible avec des moyens de production de réactifs ou pour le traitement de résidus avec des moyens de production des réactifs gazeux par des moyens électrochimiques
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