Rockwell Automation Technologies Incorporated

Edward Alan Hill

John Dyck

Juan Asenjo

Joseph A Harkulich

John Strohmenger

Bradford Henry Hegrat

Michael D Kalan

Andrew R Stump

David Alan Vasko

Taryl Jon Jasper

Jenifer Rydberg Wright

Charles Martin Rischar

Sujeet Chand

Jessica Lin Korpela

Rainer Hessmer

Stephen Nawalaniec

Subbian Govindaraj

Juergen K Weinhofer

Haithem Mansouri

Daniel S DeYoung

Keith Staninger

Matheus Bulho

William Sinner

Frank Kulaszewicz

Sal Conti

Salvatore T Conti

Michael John Pantaleano

Scott N Sandler

Ryan Cahalane

Eugene Liberman

Michael Robert Keller

David Wayne Comeau

Jonathan M Wise

Petr Ptacek

Nancy Lynn Burnham

Daniel S Deyoung

Clifford J Whitehead

Joseph Alan Kann

Juan L Asenjo

Daniel Deyoung

A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification. The simulation generator component performs operation simulations using the simulation model or modified simulation model to facilitate determining whether making the modification is appropriate, determining or predicting performance of a modified industrial automation system, determining compatibility of a modification with an industrial automation system, or determining or predicting performance of the industrial automation system when processing a work order.

Using cloud-based data for industrial simulation

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 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

A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. The service employs a performance enhancement component that analyzes the data to facilitate determining correlations between certain user interactions and favorable performance of an industrial automation system, determining user interactions that are less favorable or unsafe, determining alternative actions that a user can take to achieve a same or similar preferred operational result, generating recommendations relating to the alternative actions, determining or designing components or techniques that can automate a preferred user action, determining improved user assignments in connection with the industrial automation system, and/or generating training modules or presentations based on preferred user actions that can be used to train users to more efficiently interact with an industrial automation system to achieve improved system performance.

Using cloud-based data for industrial automation system training

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

A cloud-based virtualization generation service collects industrial-automation-system-related data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component analyzes the data and generates a virtualized industrial automation system of the industrial automation system based on the analysis results. The virtualization management component updates the virtualized industrial automation system based on operation of, and user interactions with, the industrial automation system. The virtualization management component receives user interactions with the virtualized industrial automation system to facilitate remotely interacting with and/or controlling the industrial automation system and/or generates corresponding control signals that it sends to the industrial automation system to facilitate controlling operation of the industrial automation system. The virtualization management component also customizes a user's view of the virtualized industrial automation system based on user's role, authorization, or location.

Using cloud-based data for virtualization of an industrial machine environment

Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Industrial data analytics in a cloud platform

An industrial control system may receive data associated with at least one component within an industrial automation system. The industrial control system may then determine whether the data is associated with at least one of a plurality of data tags, such that the at least one of the plurality of data tags describes at least one characteristic of the data. The industrial control system may then broadcast the data and the at least one of the plurality of data tags in a data feed channel when the data is associated with the at least one of the plurality of data tags.

Systems and methods for broadcasting data and data tags associated with an industrial automation system

A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.

Scalable analytics architecture for automation control systems

A cloud-based virtualization generation service collects industrial data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component (VMC) generates a virtualized industrial automation system of an industrial automation system based on data analysis results. The VMC facilitates remotely controlling the industrial automation system based on user interactions with the virtualized industrial automation system, and updates the virtualized industrial automation system based on collected data relating to the industrial automation system. The VMC customizes a user's view of the virtualized industrial automation system based on a user's role, authorization, location, or preferences, wherein different views of the virtualized industrial automation system with different data overlays are presented on different communication devices of different users. The VMC enables a user to filter data of a virtualized view and share information, including virtual notes, with other users.

Using cloud-based data for virtualization of an industrial automation environment with information overlays

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Chagrin Falls, OH, United States of America

Edward Alan Hill