Rockwell Automation Technologies Incorporated

Ryan Cahalane

Michael John Pantaleano

Scott N Sandler

Ronald Eric Bliss

Douglas J Reichard

Jessica Lin Korpela

Jonathan D Walter

Bruce T McCleave, Jr

Christopher Wayne Como

Kyle K Reissner

Edward Anthony Gray

Mohit Singhai

Sharon M Billi-Duran

Sharon M Billi

Andrew R Stump

Francisco Paul Maturana

Edward Alan Hill

John Dyck

Eugene Liberman

Michael Robert Keller

David Wayne Comeau

Jonathan M Wise

Petr Ptacek

Alexander B Cherpakov

Nancy Lynn Burnham

Bryan N Siafakas

Anthony Carrara

David Robert Curry

David P Rapini

Brian Alan Porter

Guangwen Sun

Bruce T Mccleave

Brian A Porter

Mohit Shinghai

A cloud-based operator training system includes a snapshot management architecture, which provides a hybrid system for generation of control system level scenarios and system-state snapshots, and which can improve the fidelity of a training simulation. By implementing the simulation system on a cloud platform, the system can generate a large and growing set of snapshot files representing various control states and corresponding process states. These files can then be leverage during operator training sessions to yield high fidelity simulated system operation.

Snapshot management architecture for process control operator training system lifecycle

The subject matter disclosed herein relates generally to industrial automation systems, and, more particularly, to collecting video streams from a variety of video devices in an industrial environment, identifying and analyzing potential problems in the industrial environment using the video streams, and presenting the video streams and data associated with the potential problem to appropriate recipients.

Analyzing video streams in an industrial environment to identify potential problems and select recipients for a display of video streams related to the potential problems

A multi-platform industrial search system facilitates indexing and searching of plant-wide data residing on multiple different data platforms. The industrial search system automatically inventories industrial devices and other data sources located throughout a plant, and identifies available data items on each data source. The search system indexes the discovered data items in a federated data model that can subsequently be searched to locate data items or tags of interest. The federated data model records references to data items found on different types of data sources, including but not limited to industrial controller programs, human-machine interface applications, data historians, device documentation stores, inventory tracking systems, and other such data sources. The search system can also initiate automated searches of the federated data model in response to detected performance issues and deliver notifications and contextual information to plant personnel.

Dynamic search engine for an industrial environment

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

An industrial data indexing system facilitates discovery and indexing of plant-wide data residing on multiple different data platforms. The indexing system automatically inventories industrial devices and other data sources located throughout a plant, and identifies available data items on each data source. The indexing system indexes the discovered data items in a federated data model that can subsequently be searched to locate data items or tags of interest. The federated data model records references to data items found on different types of data sources, including but not limited to industrial controller programs, human-machine interface applications, data historians, device documentation stores, inventory tracking systems, and other such data sources. The search system also caches selected portions of the data model to a user's personal device to allow these portion of the model to be accessed locally on the user's device without being online with the higher level indexing system.

Selective online and offline access to searchable industrial automation data

A multi-platform industrial search and notification system performs automated and manually initiate information searches across multiple disparate industrial data platforms. The search system is built on a search platform that unifies plant-wide control system information from multiple diverse sources under a common namespace, or federated data model. The system can monitor this federated data model for conditions that may warrant attention by plant personnel, and deliver proactive notifications and operational data to selected users via their personal devices. The system can customize presentation of this data based on the user's role, location, and/or other contextual information. To this end, the system can automatically generate user-specific and context-specific dashboards, widgets, or other visualizations, and render these custom visualizations to the user at appropriate times.

Automated creation of industrial dashboards and widgets

An industrial workflow tracking and identification system captures optimal employee workflows for addressing maintenance issues or operating industrial systems, and renders these workflows at appropriate times in order to guide operators and maintenance personnel through optimal sequences for carrying out operations or addressing maintenance issues. The system monitors and indexes both plant-wide system data as well as employee behaviors, and identifies correlations between operational outcomes and user workflows. In this way, the system tracks and captures optimal employee workflows for addressing particular maintenance issues, performing certain procedures, or achieving preferred production outcomes. By identifying and recording correlations between observed employee behaviors and production outcomes, the system creates a library of best practices that can be used as a training tool, as well as to provide substantially real-time guidance to maintenance staff and operators in connection with solving a problem or performing a task.

Workflow tracking and identification using an industrial monitoring system

An industrial control program development system allows a user to set metadata attributes for respective data items in the controller that indicate which of the data tags are to be monitored and collected by a separate industrial data historian system. When the industrial controller is deployed on a plant network, the data historian system reads the metadata attributes for the respective data items (e.g., data tags or data logs) defined in the controller and configures itself to collect and store data associated with the subset of data items that have been flagged for data collection. This system mitigates the need to manually configure the data historian with the identities of the data items that are to be monitored and collected for historical or reporting purposes.

Method to automate historian configuration using controller based tag meta attribute

A industrial visualization system generates augmented reality presentations for rendering on a user's wearable computer as the user traverses an industrial facility. The system can leverage data indexed in a federated data model that unifies plant-wide data from multiple diverse sources under a common namespace. By leveraging the unified plant data and interacting with a wearable computer worn by a user, the augmented reality presentation system provides automation system data, notifications, and proactive guidance to a user via modification of the user's view of his or her immediate surroundings. Such modifications can include superimposing data values or indicators on the user's view of a machine or automation system through the user's wearable computer. The system can customize presentation of this information based on the user's role, location, line of sight, type of wearable device, and/or other contextual information.

Augmented reality presentation of an industrial environment

An industrial search and diagnosis system discovers available data items across heterogeneous data platforms that make up an industrial enterprise and indexes the data items in a unified searchable namespace. The system also indexes non-textual or multimedia input, including image data, video data, and audio data. This multimedia data can be indexed by the system in association with the relevant industrial systems (e.g., machines, automation systems, devices, production lines, etc.) so that the system can provide the multimedia data to the user when the relevant industrial systems are searched or invoked. The system also accepts and processes multimedia data from as search criteria or for diagnostic analysis. In an example analysis, users can submit images, audio, or video of an industrial machine or automation system, and the diagnosis system can identify an operational abnormality based on comparative analysis performed on the multimedia information.

Multimodal search input for an industrial search platform

An automated industrial notification platform creates a link between a given industrial asset and one or more people or support entities qualified to provide support for the asset. An industrial device or machine stores electronic expert tag information that records and catalogs one or more support people or entities to be notified in response to detection of various types of performance or maintenance issues. The tag can include the experts' identities, contact information, roles relative to the industrial asset, event priorities, and other relevant information. Since the tag is stored in a memory location of the industrial asset, the tag travels with the asset so that experts are globally associated with the asset regardless of the asset's location. Notification components integrated with the industrial asset detect conditions that may require expert assistance, and send an automated notification to one or more experts identified by the tag information.

Delivery of automated notifications by an industrial asset

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Chagrin Falls, OH, United States of America

Ryan Cahalane