US 12,085,910 B2
Model asset library and recommendation engine for industrial automation environments
Jordan C. Reynolds, Austin, TX (US); John J. Hagerbaumer, Mequon, WI (US); Troy W. Mahr, Pleasant Prairie, WI (US); Thomas K. Jacobsen, Wake Forest, NC (US); and Giancarlo Scaturchio, Pisa (IT)
Assigned to Rockwell Automation Technologies, Inc., Mayfield Heights, OH (US)
Filed by Rockwell Automation Technologies, Inc., Mayfield Heights, OH (US)
Filed on Sep. 24, 2021, as Appl. No. 17/484,657.
Prior Publication US 2023/0096319 A1, Mar. 30, 2023
Int. Cl. G05B 19/042 (2006.01)
CPC G05B 19/0426 (2013.01) [G05B 2219/23077 (2013.01)] 20 Claims
OG exemplary drawing
 
1. 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.