US 12,085,910 B2Granted

Model asset library and recommendation engine for industrial automation environments

AssigneeRockwell Automation Technologies, Inc.

InventorsJordan C. Reynolds, John J. Hagerbaumer, Troy W. Mahr, Thomas K. Jacobsen, Giancarlo Scaturchio

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Abstract

Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based recommendation engine configured to, an industrial programming environment, generate a recommendation to add a component to control logic based on an existing portion of the control logic. A notification component is configured to surface the recommendation in the programming environment. A programming component is configured to, in the programming environment, add the component to the control logic. A configuration component is configured to configure the component based at least in part on the existing portion of the control logic.

Claims20

Dependent claims

A system comprising:

a memory that stores executable components;
and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a machine learning-based recommendation engine in a programming environment for editing control logic associated with an industrial automation environment, wherein the machine learning-based recommendation engine comprises at least one machine learning model trained to:
ingest an input comprising an existing portion of the control logic and historical operational data produced during runtime in at least one other industrial automation environment;
identify relevant devices for the industrial automation environment from a component library based at least in part on the existing portion of the control logic and the historical operational data;
and
generate a recommendation to add a device selected from the relevant devices to the control logic, wherein the device represents a physical device in the industrial automation environment;
a notification component configured to surface the recommendation in the programming environment;
a programming component configured to, in the programming environment, add the device to the control logic;
and
a configuration component configured to, in the programming environment, configure the device based at least in part on the existing portion of the control logic.

9 more elements

A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:

in a programming environment for editing control logic associated with an industrial automation environment, ingesting, by at least one machine learning model of a machine learning-based recommendation engine, an input comprising an existing portion of the control logic and historical operational data produced during runtime in at least one other industrial automation environment;
identifying relevant devices for the industrial automation environment from a component library based at least in part on the existing portion of the control logic and the historical operational data;
and
generating a recommendation to add a device selected from the relevant devices to the control logic, wherein the device represents a physical device in the industrial automation environment;
surfacing the recommendation in the programming environment;
in the programming environment, adding the device to the control logic;
and
in the programming environment, configuring the device based at least in part on the existing portion of the control logic.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/484,657Filed 24 Sep 2021
Published: US 2023/0096319 A130 Mar 2023
Granted: 10 Sep 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G05B 19/0426
Using digital processors: programming the control sequence
G05B 19/4188
Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible…:…
G06F 8/20
Arrangements for software engineering: software design
G06F 8/34
Creation or generation of source code: graphical or visual programming