Rockwell Automation Technologies Incorporated

Joachim Thomsen

Stephen C Briant

Noel Henderson

Manuel Sukerman

Sujeet Chand

Bijan Sayyarrodsari

Juergen K Weinhofer

Robert J Miklosovic

Scott A Miller

Brian Fast

Mark A Chaffee

Robert Guenther

John B Dart

Bruce E Hammon

Frank Jordan

Steven C Briant

Martin Petrick

Daniel J Aardsma

Derrick Tapscott

James Butler

An industrial data presentation system leverages structured data types defined on industrial devices to generate and deliver meaningful presentations of industrial data. Industrial devices are configured to support structured data types referred to as basic information data types (BIDTs) comprising a finite set of structured information data types, including a rate data type, a state data type, an odometer data type, and an event data type. The BIDTs can be referenced by both automation models of an industrial asset and non-automation models of the asset, allowing data points of both types of models to be easily linked using a common data source nomenclature.

Industrial automation information contextualization method and system

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

A state machine management component that executes on an edge device within an industrial facility is capable of discovering state machines defined on industrial controllers and translating the state machine definitions to information models that allow the state machines to be discovered and utilized by remote client applications. The edge-level information models allow the client applications to render or consume the state machine data generated by the controller, graphically render the state machines, submit commands, or set values, to the controller as state machine commands via the edge device. The edge-level system thus allows client applications with no prior knowledge of the control-level state machines to discover, retrieve, and interact with state machines defined on the industrial controllers.

Edge application discovering and management state machine objects in automation controllers for MES and DCS applications

Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

AI extensions and intelligent model validation for an industrial digital twin

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Krefeld, Germany

Joachim Thomsen