Rockwell Automation Technologies Incorporated

Juergen K Weinhofer

Subbian Govindaraj

Michael D Kalan

Andrew R Stump

Charles Martin Rischar

Edward Alan Hill

Haithem Mansouri

Daniel S DeYoung

Keith Staninger

Matheus Bulho

Frank Kulaszewicz

William Sinner

Sujeet Chand

David Alan Vasko

Kendal R Harris

Sharath Chander Reddy Baddam

Douglas J Reichard

Bijan Sayyarrodsari

Michael John Pantaleano

Kyle A Crum

Jacob Strickler Baker

Andrew J Ellis

Stephen C Briant

Scott A Miller

Tim S Biernat

Noel Henderson

Manuel Sukerman

Bruce E Hammon

Ka H Lin

Kenneth William Batcher

Joachim Thomsen

Timothy S Biernat

Mark A Chaffee

Jatin P Bhatt

William C Schwarz

John Michael Keller

Ka-Hing Lin

Donald A Westman

Daniel S Deyoung

Andrew James Ellis

Daniel Deyoung

Blockchain-enabled digital twins for industrial automation systems (e.g., using a computerized tool) are enabled. For example, a non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause an industrial device comprising a processor to perform operations, the operations comprising: determining an equivalency of the configuration of the industrial automation component based on a comparison of the first configuration fingerprint with a second configuration fingerprint of the configuration of the industrial automation component generated at a second time, subsequent to the first time, determining an equivalency of the configuration of the industrial automation component based on a comparison of the first configuration fingerprint with a second configuration fingerprint of the configuration of the industrial automation component generated at the second time, and generating an output indicative of the equivalency of the digital twin and the equivalency of the configuration.

Blockchain-enabled digital twins for industrial automation systems

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.

Industrial blockchain enabled automation control

A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Industrial data services platform

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.

Systems and methods for balancing loads in an industrial automation system

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.

Systems and methods for incorporating proxy components in an industrial automation system

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.

Artificial intelligence channel for industrial automation

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.

Systems and methods for adjusting operations of an industrial automation system based on multiple data sources

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. The blockchain-enabled industrial devices can be configured to generate multiple versions of a product or machine's blockchain having respective different access permissions, allowing public and private industrial data to be segregated between public and private industrial blockchains.

Managing blockchains in an industrial facility based on firmware change

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.

Industrial automation control program generation from computer-aided design

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.

Contextualization of industrial data at the device level

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.

Systems and methods for adapting a user interface based on a profile

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Mayfield Heights, OH, United States of America

Juergen K Weinhofer