Abstract: A phased array ultrasonic testing device for positioning first and second phased array probes relative to a surface of a material to be tested. The testing device includes a first wedge configured to receive and orient the first phased array probe relative to the surface. A second wedge is configured to receive and orient the second phased array probe relative to the surface. A coupling structure is mounted to the first and second wedges and configured to selectively provide for the first and second wedges to move parallel to one another in a first direction and restrict relative movement of the first and second wedges in other directions.

Abstract: A reversible adhesive apparatus includes a base and an adhesive layer. The base is configured with a series of concave and convex shapes at a top surface of the base according to some embodiments. The base is configured with a raised portion and a surrounding portion at a top surface of the base according to some other embodiments. The adhesive layer is disposed on top of the base. The adhesive layer configured to provide reversible adhesion on a top surface of the adhesive layer.

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 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 transportation system includes one or more movers and a robot system. The robot system is configured to load one or more items on the surface of respective movers and unload the one or more items from the surface of respective movers. Each of the one or more movers includes a first surface configured to provide reversible adhesion between the mover and an item loaded on the mover. The reversible adhesion of the surface is activated at loading and deactivated at unloading.

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: 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: 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 reversible adhesive apparatus includes a base and an adhesive layer. The base is configured with a series of concave and convex shapes at a top surface of the base according to some embodiments. The base is configured with a raised portion and a surrounding portion at a top surface of the base according to some other embodiments. The adhesive layer is disposed on top of the base. The adhesive layer configured to provide reversible adhesion on a top surface of the adhesive layer.

Abstract: An industrial transportation system includes one or more movers and a robot system. The robot system is configured to load one or more items on the surface of respective movers and unload the one or more items from the surface of respective movers. Each of the one or more movers includes a first surface configured to provide reversible adhesion between the mover and an item loaded on the mover. The reversible adhesion of the surface is activated at loading and deactivated at unloading.

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: 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: 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.