INDUSTRIAL ARTIFICIAL INTELLIGENCE SYSTEM NODE NETWORK OPTIMIZATION

    公开(公告)号:US20250045602A1

    公开(公告)日:2025-02-06

    申请号:US18363138

    申请日:2023-08-01

    Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.

    DYNAMIC INDUSTRIAL ARTIFICIAL INTELLIGENCE CONFIGURATION AND TUNING

    公开(公告)号:US20250044767A1

    公开(公告)日:2025-02-06

    申请号:US18363197

    申请日:2023-08-01

    Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.

    Automated monitoring using image analysis

    公开(公告)号:US12165302B2

    公开(公告)日:2024-12-10

    申请号:US17482040

    申请日:2021-09-22

    Abstract: A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving image data after an operation is performed by an industrial automation device on a product; analyzing the image data based an object-based image analysis (OBIA) model to classify the product as one of a plurality of conditions related to manufacturing quality and the OBIA model includes property layers associated with features related to a manufacturing of the product; determining whether the one of the conditions indicates an anomaly being present in the product; sending a notification indicative of the one of the plurality of conditions is presently associated with the product; identifying a property layer associated with classifying the one of the plurality of conditions; and updating the OBIA model based on the property layer and the input indicative of the anomaly being incorrectly associated with the product.

    SYSTEMS AND METHODS FOR RETRAINING A MODEL A TARGET VARIABLE IN A TIERED FRAMEWORK

    公开(公告)号:US20230016084A1

    公开(公告)日:2023-01-19

    申请号:US17932113

    申请日:2022-09-14

    Abstract: A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

    Systems and methods for retraining a model a target variable in a tiered framework

    公开(公告)号:US11449047B2

    公开(公告)日:2022-09-20

    申请号:US16146681

    申请日:2018-09-28

    Abstract: A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

    INDUSTRIAL ARTIFICIAL INTELLIGENCE MODEL GRAPHS

    公开(公告)号:US20250044778A1

    公开(公告)日:2025-02-06

    申请号:US18363177

    申请日:2023-08-01

    Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.

    SYSTEM LEVEL INDUSTRIAL ARTIFICIAL INTELLIGENCE AGGREGATION OF BASELINE DATA DEVIATION

    公开(公告)号:US20250044762A1

    公开(公告)日:2025-02-06

    申请号:US18363291

    申请日:2023-08-01

    Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.

    INDUSTRIAL ARTIFICIAL INTELLIGENCE MODEL AGGREGATION

    公开(公告)号:US20250036098A1

    公开(公告)日:2025-01-30

    申请号:US18359341

    申请日:2023-07-26

    Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.

    Systems and methods for retraining a model a target variable in a tiered framework

    公开(公告)号:US12174624B2

    公开(公告)日:2024-12-24

    申请号:US17932113

    申请日:2022-09-14

    Abstract: A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

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