Rockwell Automation Technologies Incorporated

Rockwell Technologies, Inc.

Allen-Bradley Company, LLC

Sito Processing LLC

David Alan Vasko

Kenwood H Hall

Subbian Govindaraj

Robert J Kretschmann

Charles Martin Rischar

Raymond John Staron

Matthew Robert Ericsson

Jessica Lin Korpela

Kyle K Reissner

Jimi R Michalscheck

Kelly A Michalscheck

Andrew Wilber

Matthew W Fordenwalt

Michael D Kalan

Taryl Jon Jasper

Sujeet Chand

Thong T Nguyen

Paul R D'Mura

Alex Laurence Nicoll

Richard J Grgic

John Joseph Baier

David Michael Callaghan

Brian Alan Batke

Juergen K Weinhofer

David Dale Brandt

Bijan Sayyarrodsari

Paul David Schmirler

David W Farchmin

Michael John Pantaleano

Francisco Paul Maturana

Kenneth William Batcher

Steven T Haensgen

Anthony Gerard Gibart

Ramdas M Pai

Derek William Jones

Tracy M Clark

Robert H Schmidt

Ronald Eric Bliss

Rainer Hessmer

Richard Alan Morse

Yongyao Cai

Sivaram Balasubramanian

Michael Lee Gasperi

Edward Alan Hill

Douglas Robert Bodmann

Nathan Joseph Molnar

Joseph G Vazach

John Strohmenger

Philip John Kaufman

Michael R Keller

John J Jauquet

Joseph A Lenner

Kyle A Crum

Eugene Liberman

Rebecca J Herr

Scot A Tutkovics

Daniel Baliey Seger

Douglas R Wylie

Brian J McMullen

Kerry W Vandesteeg

Andrew J Ellis

Sharath Chander Reddy Baddam

David W Siegler

Angel Sustaeta

Timothy S Biernat

Chirag L Malkan

David A Snyder

Richard Arthur Morgan

John B Dart

Ka H Lin

Timothy Patrick Boppre

Daniel P Noonen

Francisco P Maturana

Steven J Scott

Paul G Kucharski

Neil R Bentley

David Main-Reade

Edward C Korsberg

Michael D Cook

Katherine Sokolnicki

John Nesi

Paul David Brooks

Yutao Wang

Edward C Hopsecger

Michael Robert Keller

Anatoly Moldovansky

Robert E Lounsbury

Gary W Baczkowski

Petr Ptacek

Salvatore T Conti

Kevin E Colloton

Ka-Hing Lin

Michael S Pelley

Darren R Klug

Timothy F Roback

Jeffrey Kian Chang

Stephen J Vandenberg

Stephen John VandenBerg

Jeffrey Kian Cheng

Edward Korsberg

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.

Performance-based smart contracts in industrial automation

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 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. The method includes determining that the abnormal operating condition correlates to the abnormal data traffic condition, and sending a security alert in response to determining that the abnormal operating condition correlates to the abnormal data traffic condition.

Security and safety of an industrial operation using opportunistic sensing

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. The method includes identifying an abnormal operating condition of the component based on a comparison between additional sensor information from the additional sensor and the operational parameter, and sending an alert with the abnormal operating condition.

Product lifecycle management

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.

Reversible adhesive apparatus

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

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. The method includes sending an alert with the abnormal operating condition.

Aggregate and correlate data from different types of sensors

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.

Systems and methods for a multi-purpose sensing device for industrial automation equipment

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 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.

Systems and methods of applying reversible adhesion in a transportation system

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.

Lockout, tagout audit system

In one embodiment, a non-transitory computer readable medium may include computer-executable instructions that, when executed by a processor, may cause processor to receive a set of user data associated with a user that is attempting to access an electronic lock, receive a request to actuate a locking mechanism of the electronic lock configured to prevent the user from accessing a machine in an industrial automation system, actuate the locking mechanism in response to the request and the set of user data corresponding to an expected set of data, store a log of the request and the set of user data, and send the log to a cloud-based computing system.

Systems and methods for virtually tagging and securing industrial equipment

Growing community of inventors

Macedonia, OH, United States of America

David Alan Vasko