Abstract: An industrial control system may receive processing information from at least two control systems associated with at least two components within an industrial automation system. The processing information may include a processing load value for each of the at least two control systems. The industrial control system may then distribute processing loads associated with the at least two control systems when a total processing load between the at least two control systems is unbalanced.

Abstract: An industrial automation component may receive design information associated with an arrangement of one or more industrial automation components in an industrial automation system and identify a first portion of the industrial automation components in the industrial automation system that is unable to interface with a second portion of industrial automation components based on the design information. The industrial automation component may then generate a list of industrial automation components based on the first portion of the industrial automation components and the second portion of industrial automation components, wherein each of the list of industrial automation components is configured to translate a first set of data associated with the first portion of the industrial automation components to a second set of data configured to be at least partly interpretable by the second portion of industrial automation components.

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 automation component may receive a first set of data associated with the industrial automation component, such that the industrial automation component is associated with a first industrial automation system. The industrial automation component may then receive a second set of data associated with one or more other industrial automation components, such that the one or more other industrial automation components are associated with one or more other industrial automation systems. The industrial automation component may then identify one or more similar patterns in the first set of data and the second set of data and adjust one or more operations of the industrial automation component based on the similar patterns.

Abstract: Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information.

Abstract: An industrial control programming development platform simplifies generation of an industrial control program and associated tag definitions by generating at least a portion of the control program and tag definitions based on analysis of digital engineering drawings of an automation system to be monitored and controlled. This drawing-based program generation includes creation and configuration of smart data tags that model and contextualize controller data at the device level for processing by higher level analytic systems. This device-level contextualization can be based in part on inferences drawn from the digital engineering drawings.

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 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 automation component may receive identification information associated with a user of the industrial automation component and determine whether a profile that corresponds to the identification information exists in a database. When the profile does not exist, the industrial automation component may receive a graphical interface template configured to be displayed on a graphical user interface (GUI) associated with the industrial automation component, one or more parameter names configured to associate one or more measurements acquired by the industrial automation component, pre-load information comprising data configured to enable the industrial automation component to operate, or any combination thereof. The industrial automation component may then and generate a user interface view for the GUI based on the graphical interface template, the parameter names, the pre-load information, or any combination thereof.

Abstract: An industrial automation component may receive design information associated with an arrangement of one or more industrial automation components in an industrial automation system and identify a first portion of the industrial automation components in the industrial automation system that is unable to interface with a second portion of industrial automation components based on the design information. The industrial automation component may then generate a list of industrial automation components based on the first portion of the industrial automation components and the second portion of industrial automation components, wherein each of the list of industrial automation components is configured to translate a first set of data associated with the first portion of the industrial automation components to a second set of data configured to be at least partly interpretable by the second portion of industrial automation components.

Abstract: An automation control system is provided that includes one or more components. The components include an embedded execution engine that is configured to execute one or more commands based upon data communicated to the one or more components from another component of the automation control system. The data is representative of a change to an object in the control system.

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 exposed to higher level systems for creation of analytic models that can be used to analyze data from the industrial device relative to desired business objectives.

Abstract: An industrial automation component may receive design information associated with an arrangement of one or more industrial automation components in an industrial automation system and identify a first portion of the industrial automation components in the industrial automation system that is unable to interface with a second portion of industrial automation components based on the design information. The industrial automation component may then generate a list of industrial automation components based on the first portion of the industrial automation components and the second portion of industrial automation components, wherein each of the list of industrial automation components is configured to translate a first set of data associated with the first portion of the industrial automation components to a second set of data configured to be at least partly interpretable by the second portion of industrial automation components.

Abstract: A human-machine interface (HMI) development platform simplifies generation of an industrial visualization application by generating at least a portion of the visualization application based on analysis of digital engineering drawings of an automation system to be monitored and controlled. This drawing-based approach includes creation of HMI interface displays comprising animated graphics representing industrial assets represented in the drawings, and setting animation data sources for the graphics based on analysis of the drawings.

Abstract: An industrial control programming development platform simplifies generation of an industrial control program and associated tag definitions by generating at least a portion of the control program and tag definitions based on analysis of digital engineering drawings of an automation system to be monitored and controlled. This drawing-based program generation includes creation and configuration of smart data tags that model and contextualize controller data at the device level for processing by higher level analytic systems. This device-level contextualization can be based in part on inferences drawn from the digital engineering drawings.

Abstract: An industrial automation component may receive a first set of data associated with the industrial automation component, such that the industrial automation component is associated with a first industrial automation system. The industrial automation component may then receive a second set of data associated with one or more other industrial automation components, such that the one or more other industrial automation components are associated with one or more other industrial automation systems. The industrial automation component may then identify one or more similar patterns in the first set of data and the second set of data and adjust one or more operations of the industrial automation component based on the similar patterns.

Abstract: An industrial control system may receive processing information from at least two control systems associated with at least two components within an industrial automation system. The processing information may include a processing load value for each of the at least two control systems. The industrial control system may then distribute processing loads associated with the at least two control systems when a total processing load between the at least two control systems is unbalanced.

Abstract: Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information.

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.