Rockwell Automation Technologies Incorporated

Braun C Brennecke

Francisco Paul Maturana

Abhishek Mehrotra

Timothy R Brennan

Richard S Turk

Evan J Kausalik

John D Mayer

Tyler C Tamburlin

Steven P Taylor

Mikica Cvijetinovic

Gregory J Vance

Jay W Schiele

Dennis Jinghui Luo

Robert Nunoo

A non-transitory computer-readable medium may include computer-executable instructions that, when executed, cause a processor to collect a portion of data associated with an asset from one or more sources based on a request received from a digital representation associated with the asset. The digital representation may perform a first set of simulations related to one or more operations of the asset over time. The processor may then generate a plurality of aligned datasets based the portion of the data, the one or more sources, and an identity of the asset. The processor may also aggregate the plurality of aligned datasets into a single dataset and transmit the single dataset to the digital representation to perform a second set of simulations based on the single dataset.

Systems and methods for industrial information solutions and connected microservices

Data reduction services are implemented on one or more nodes of an IIoT data pipeline to intelligently determine an appropriate data reduction strategy based on characteristics of the incoming data. In one or more embodiments, data reduction components on the pipeline node or on an edge device define different data filtering rules or algorithms that are selectively applied to streaming time-series data based on a probability distribution of the data. The data pipeline node performs real-time distribution analysis on the streaming data to determine whether the data has a unimodal distribution, a multimodal distribution, or no mode, and selects one of the data filtering rules based on this determined probability distribution. In this way, the data is intelligently reduced in a manner that retains critical information within the reduced data set while achieving a high level of data reduction.

Multimodal data reduction agent for high density data in IIoT applications

A monitor provides run time monitoring of the input values to a signature analyzer having a machine-learning engine to compare statistics measuring the domain of those input values to the domain of input values used in the training set of the machine-learning engine. This comparison yields an indication of whether retraining of the machine-learning engine is required without analysis of the output of the machine-learning engine algorithm or the need for errors to become manifest.

System for detecting data drift in machine-learning process monitoring

A non-transitory computer-readable medium includes instructions that, when executed, cause one or more processors of a first electronic device to receive data generated by a plurality of components of an industrial automation system and characterize one or more portions of the data by applying metadata to the one or more portions of the data. The metadata enables a second electronic device receiving the data to determine one or more contexts of the one or more portions of the data. Furthermore, the computer-executable instructions, when executed, cause the one or more processors to rearrange an order of the one or more portions of the data and cause the characterized and rearranged data to be sent to the second electronic device.

Common data pipeline for sharing data associated with industrial automation systems

A non-transitory computer-readable medium includes instructions that, when executed, cause one or more processors to perform operations. The operations include receiving a first request to access a digital representation that is assigned to an asset of an industrial automation system and includes code specific to operating, maintaining, or diagnosing the asset. The operations also include verifying a security access for the first request, providing the computing device access to the digital representation, receiving an input to modify the code from the computing device and, performing one or more simulations based on the modified code by utilizing the digital representation. Additionally, the operations include sending one or more simulation results to the computing device, receiving, from the computing device, a second request for pushing the modified code to the asset, and in response to receiving the second request, causing the modified code to be sent to the asset.

Systems and methods for data lifecycle management with code content optimization and servicing

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Pittsburgh, PA, United States of America

Braun C Brennecke