Abstract: A system includes processing circuitry and a memory. The memory stores instructions that, when executed by the processing circuitry, cause the processing circuitry to receiving operational data captured from an industrial automation system performing an industrial automation process, model the industrial automation process based on the operational data, model one or more adjustments to the industrial automation process, identify that the modeling of the one or more adjustments to the industrial automation process indicates that the one or more adjustments to the industrial automation process improve one or more sustainability metrics for the industrial automation process, generate one or more sustainability recommendations to implement the one or more adjustments to the industrial automation process, and implement the one or more adjustments to the industrial automation process.

Abstract: A mover system includes a track, a plurality of movers configured to move along the track, and one or more processors configured to determine a first amount of energy consumption associated with a first portion of the plurality of movers and a second amount of energy consumption associated with a second portion of the plurality of movers and determine a first emissions per unit for each of the first portion of the plurality of movers based on the first amount of energy consumption and a second emissions per unit for each of the second portion of the plurality of movers based on the second amount of energy consumption. The one or more processors are also configured to determine one or more operating settings for the mover system based on the first emissions per unit, the first emissions per unit, or both and send one or more commands to the first number of the plurality of movers, the second number of the plurality of movers, or both based on the one or more operating settings.

Abstract: A system includes one or more automation devices and a computing system. The computing system is configured to receive motion data associated with a transit of a plurality of products, associate a first portion of the motion data to a first product of the plurality of products, and determine motion energy consumption data for the first product based on the first portion of the motion data. Further, the computing system is configured to receive machine data associated with one or more operations performed on the plurality of products at a location, associate a second portion of the machine data to the first product, and determine energy consumption data based on the motion energy consumption data and the machine energy consumption data. Moreover, the computing system is configured to send one or more control signals to the one or more automation devices based on the energy consumption 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: 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: For automatic device addressing, a processor configures each node device on a serial network for an order measurement. The processor further measures an order parameter for each of the node devices. The processor determines an order number of each of the node devices based on the order parameter for the node device.

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: For automatic device addressing, a processor configures each node device on a serial network for an order measurement. The processor further measures an order parameter for each of the node devices. The processor determines an order number of each of the node devices based on the order parameter for the node device.

Abstract: An industrial security policy configuration system generates and implements security policies for industrial automation systems based on design data for the industrial systems generated by device manufacturers, system integrators, original equipment manufacturers, or the owners of the industrial assets during the design of the industrial systems. the collected design data to a security rule set defining device-level communication privileges. The system translates the collected design data to a security rule set defining device-level communication privileges, which are then translated to a comprehensive set of security policies customized to the requirements of the industrial systems represented by the design data. By leveraging the rich set of available design data to identify or infer security requirements and generate suitable security configurations, the system can mitigate the need to manually configure security policies based on human judgments regarding normal and abnormal network traffic.

Abstract: A method executable by at least one processor includes receiving image data representative of an industrial equipment assembly, identifying properties associated with the industrial equipment assembly based on the image data, identifying a set of industrial equipment assemblies associated with the industrial equipment assembly based on the properties associated with the industrial equipment assembly and data stored in a database, and categorizing the set of industrial equipment assemblies based on the data associated with the industrial equipment assemblies.

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 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.