Abstract: Industrial automation blockchain data management (e.g., using a computerized tool) is enabled. For example, a system can comprise: a transfer component that, using a defined conversion algorithm, converts first data from a first industrial blockchain to second data applicable to a second industrial blockchain, other than the first industrial blockchain, a blockchain component that writes the second data to the second industrial blockchain, and a user interface component that renders an output indicative of the writing of the second data to the second industrial blockchain.

Abstract: Performance-based smart contracts in industrial automation (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores executable components; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a blockchain component that stores data representative of an output of an industrial automation device to an industrial blockchain, and an execution component that, in response to the output satisfying a smart contract stored on the industrial blockchain, facilitates execution of an element of the smart contract.

Abstract: Industrial security using blockchain or NFTs (e.g., using a computerized tool) is enabled. For example, an industrial system can comprise: a memory that stores executable components, and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising a minting component that mints a non-fungible token associated with a product generated using industrial automation equipment, wherein the non-fungible token comprises a first verification element, and a verification component that: verifies the product based on a comparison of a first aspect of the product to the first verification element, and generates an output representative of the comparison.

Abstract: Industrial blockchain digital twin change management (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a blockchain component that stores a digital twin fingerprint for a digital twin of an industrial automation system to an immutable blockchain ledger and stores a configuration fingerprint representative of a configuration of the industrial automation system to the immutable blockchain ledger, wherein the digital twin fingerprint is associated with the configuration fingerprint, a change component that determines a change to the industrial automation system, and an update component that facilitates creation of an updated digital twin, representative of the change, and updates the digital twin fingerprint and the configuration fingerprint based on the change.

Abstract: Industrial blockchain enabled automation control (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a contract component that determines whether a product generated using industrial automation equipment satisfies a defined condition of a smart contract, and an execution component that, in response to product being determined to satisfy the defined condition, executes a defined term of the smart contract.

Abstract: Industrial blockchain enabled automation control (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a minting component that, based on a manufacturing criterion associated with a product being determined to be satisfied, mints a non-fungible token associated with the product, wherein the non-fungible token comprises authenticity data representative of a birth certificate for the product, and a blockchain component that registers the non-fungible token with a blockchain.

Abstract: Blockchain-enabled digital twins for industrial automation systems (e.g., using a computerized tool) are enabled.

Abstract: Industrial blockchain enabled automation control (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: an authorization component that determines, according to a defined authorization criterion, authorization of a request for a license for industrial automation software, and a license component that, in response to the determination by the authorization component that the request is authorized, mints a non-fungible token comprising the license for the industrial automation software.

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: In one embodiment, a multi-purpose sensor may couple to a machine operating in an industrial environment and include numerous sensors disposed within the multi-purpose sensor to acquire sets of data associated with the machine or an environment surrounding the machine. A first portion of the sets of data may include historical sensor measurements over time for each of the sensors, and a second portion of the sets of data may include sensor measurements subsequent to when the first portion is acquired for each of the sensors. A processor of the multi-purpose sensor may determine a baseline collective signature based on the first portion, determine a subsequent collective signature based on the second portion, determine whether the collective signatures vary, and generate signals when a variance exists. The signals may cause a computing device, a cloud-based computing system, and/or a control/monitoring device to perform various actions.

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: A lockout tagout auditing and guidance system provides a mobile device that can guide an individual to particular tags either to perform the operations required for lockout procedures or to perform an auditing activity. In both cases, images of the tags are obtained and imaged and tag locations compared to corresponding information in the database to assess the existence and clarity of the tags for auditing purposes.

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.