Rockwell Automation Technologies Incorporated

Jessica Lin Korpela

Kyle K Reissner

John Strohmenger

Matthew W Fordenwalt

Rainer Hessmer

Juan Asenjo

Joseph A Harkulich

Edward Alan Hill

Bradford Henry Hegrat

John Dyck

Stephen Nawalaniec

Jenifer Rydberg Wright

David Alan Vasko

Matthew Robert Ericsson

Jimi R Michalscheck

Kelly A Michalscheck

Andrew Wilber

John J Jauquet

Sal Conti

Douglas J Reichard

Ronald Eric Bliss

Michael John Pantaleano

Jonathan D Walter

Scott N Sandler

Ryan Cahalane

Christopher Wayne Como

Bruce T McCleave, Jr

Edward Anthony Gray

Mohit Singhai

Jan Pingel

Douglas B Weber

Stephen L Hickox

Eric Rehl

Barry M Jereb

Amit R Sathe

Salvatore T Conti

Sharon M Billi-Duran

Sharon M Billi

David J Lillie

Brian Dipert

Tracy L Swartzendruber

Jessica M Truong

Peter Baron

Jeffrey L Kibbey

Lisa J Smith

Mohit Shinghai

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

The technology disclosed herein enables automation of component sourcing when designing a process system in an industrial environment. In a particular embodiment, a method includes receiving design specifications of a process system in an industrial environment via a system design application. The method further includes determining components to implement the process system from the design specifications and identifying a first subset of the components for sourcing from an external provider. The method also includes populating the first subset into a component sourcing application.

Automated component sourcing based on process system design

Various embodiments of the present technology provide an integrated platform that provides optimization tools that can be used across multiple lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take specified designs in a presale phase and easily open up that information in a corresponding tool to address needs in the post-sale phase to allow for all device commissioning, programming, and configuration without any importing, exporting, or recreation of presale files. Various embodiments use a common, cross-platform data file that links activity within each lifecycle phase. As such, any layouts or designs can be easily opened up within the platform by users with differing roles. Moreover, if in the post-sale phase, a control engineer decides that a specific aspect of control system was left out, the control engineer can summon the capability of the presale phase to expand the system.

Lifecycle data files for industrial automation project optimization

A cloud-based modeler component that generates interactive models of an industrial automation system(s) (IAS(s)) is presented. An interactive model facilitates remote viewing of, interaction with, troubleshooting of problems with, or optimization of industrial assets of an IAS. The modeler component polls industrial assets via cloud gateways to obtain information relating to the industrial assets to identify industrial assets of the IAS and relationships with other industrial assets or can receive information from a communication device that obtains information relating to legacy industrial assets to identify those legacy assets and their relationships. The modeler component generates an interactive model of the industrial assets of the IAS based on the information. The modeler component also can discover new industrial assets added to the IAS, can receive a pre-deployed model of an industrial asset from the industrial asset or another source, and can incorporate the pre-deployed model in the interactive model.

Modeling of an industrial automation environment in the cloud

A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations. The analytics engine also can determine when there is a deviation or potential of deviation from desired system performance by an industrial asset or employee, and provide a notification, a recommendation, or an instruction to rectify or avoid the deviation.

Cloud-based analytics for industrial automation

Automated component sourcing based on process systems design

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

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

A cloud-based industrial controller that controls devices, processes, and other assets of an industrial automation system via control algorithms that execute on a cloud platform is presented. A cloud-based collection component collects information from the industrial automation system via cloud gateways associated with the industrial automation system or extrinsic data sources. The cloud-based industrial controller can monitor and analyze the information, generate control instructions based on the analysis results, and communicate the control instructions to the devices, processes, and/or other assets of the industrial automation system to control operation of the industrial automation system. The cloud-based industrial controller also can interface with a industrial plant-based industrial controller, wherein the cloud-based industrial controller can determine supplemental control instructions to the industrial plant-based industrial controller, based on the information, including extrinsic information, to assist in controlling the industrial automation system and control decision-making.

Cloud-based industrial controller

Various embodiments of the present technology provide an integrated platform that provides personalized experiences for users of an integrated platform during various phases of an industrial automation project lifecycle. In accordance with various embodiments, the integrated platform can create personalized experiences for different users based on one or more assigned roles. The roles can include company roles, industry roles, job roles, location-based roles, personalized roles, and the like. For example, some company that only purchases certain brands of components for an industrial automation project can indicate that preference. During interactions with the integrated platform, any user associated with that company (e.g., via the company role) will only be presented with those preferences. As another example, generic requests received via one interaction channel can be analyzed and personalized, specific responses can be generated based on activity within other interaction channels.

Industrial automation personalization engine

Growing community of inventors

Milwaukee, WI, United States of America

Jessica Lin Korpela