Abstract: A method for security and safety of an industrial operation includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from at least a portion of the plurality sensors is used for functionality of a plurality of components of the industrial operation. The method includes monitoring data traffic of the industrial operation, and deriving a baseline signature from the sensor information. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition of the industrial operation based on a comparison between additional sensor information from the plurality of sensors and the baseline signature and identifying an abnormal data traffic condition.

Abstract: A method for correlating data from different sensors for product lifecycle management includes receiving sensor information from an additional sensor of a plurality of sensors of an industrial operation. The additional sensor is different from component sensors used for functionality of a component of the industrial operation. Sensor information from the additional sensor monitors conditions of a portion of the industrial operation different from sensor information of the component sensors used for the functionality of the component. The method includes deriving, using the sensor information, a correlation between an operational parameter of the component and sensor information of the additional sensor. The operational parameter is related to a predicted operational lifetime of the component.

Abstract: An industrial data broker system receives contextualized industrial data from one or more industrial devices that support data modeling at the device level. The received industrial data is augmented with contextualization metadata that defines correlations between the data relevant to an analytical objective, and labels specifying analytic topics to which each data item is relevant. The broker system allows external systems, such as analytic systems, to subscribe to topics of interest, and streams a subset of contextualized device data relevant to the topic of interest to the external system for analysis.

Abstract: A method for correlating data from sensors includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from component sensors is used for functionality of a component of the industrial operation and sensor information from additional sensors monitor conditions of a portion of the industrial operation different from the component. The method includes deriving, using the sensor information, correlations between component sensors and additional sensors and deriving a baseline signature from the sensor information and the correlations. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition based on a comparison between additional sensor information and the baseline signature. The sensor information is used differently for functionality of the component than for deriving the correlations and baseline signature and identifying the abnormal operating condition.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: A method for security and safety of an industrial operation includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from at least a portion of the plurality sensors is used for functionality of a plurality of components of the industrial operation. The method includes monitoring data traffic of the industrial operation, and deriving a baseline signature from the sensor information. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition of the industrial operation based on a comparison between additional sensor information from the plurality of sensors and the baseline signature and identifying an abnormal data traffic condition.

Abstract: A method for correlating data from different sensors for product lifecycle management includes receiving sensor information from an additional sensor of a plurality of sensors of an industrial operation. The additional sensor is different from component sensors used for functionality of a component of the industrial operation. Sensor information from the additional sensor monitors conditions of a portion of the industrial operation different from sensor information of the component sensors used for the functionality of the component. The method includes deriving, using the sensor information, a correlation between an operational parameter of the component and sensor information of the additional sensor. The operational parameter is related to a predicted operational lifetime of the component.

Abstract: A method for correlating data from sensors includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from component sensors is used for functionality of a component of the industrial operation and sensor information from additional sensors monitor conditions of a portion of the industrial operation different from the component. The method includes deriving, using the sensor information, correlations between component sensors and additional sensors and deriving a baseline signature from the sensor information and the correlations. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition based on a comparison between additional sensor information and the baseline signature. The sensor information is used differently for functionality of the component than for deriving the correlations and baseline signature and identifying the abnormal operating condition.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise. The multi-level industrial analytic system can also further refine modeled industrial data as the data moves upward through the system (e.g., from the device level to higher levels).

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: An industrial data broker system receives contextualized industrial data from one or more industrial devices that support data modeling at the device level. The received industrial data is augmented with contextualization metadata that defines correlations between the data relevant to an analytical objective, and labels specifying analytic topics to which each data item is relevant. The broker system allows external systems, such as analytic systems, to subscribe to topics of interest, and streams a subset of contextualized device data relevant to the topic of interest to the external system for analysis.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.

Abstract: An industrial visualization system generates and delivers virtual reality (VR) and augmented reality (AR) presentations of industrial facilities to wearable appliances to facilitate remote or enhanced interaction with automation systems within the facility. VR presentations can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility. The system can selectively render a scaled down view that renders the facility as a 3D scale model, or as a first-person view that renders the facility as a full-scale rendition that simulates the user's presence on the plant floor. Camera icons rendered in the VR presentation can be selected to switch to a live video stream generated by 360-degree cameras within the plant. The system can also render workflow presentations that guide users through the process of correcting detected maintenance issues.