US 12,242,233 B2
Machine learning models for asset optimization within 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. 17/484,461.
Prior Publication US 2023/0097885 A1, Mar. 30, 2023
Int. Cl. G05B 13/04 (2006.01); G05B 13/02 (2006.01)
CPC G05B 13/042 (2013.01) [G05B 13/0265 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for optimizing industrial control, the system comprising:
one or more processors; and
one or more memories having stored thereon instructions that, upon execution by the one or more processors, cause the one or more processors to:
store a library of machine learning models for optimizing parameters in an industrial process in an industrial automation environment,
via a block-based programming environment comprising a user interface for creating and editing control logic comprising iconic representations of automated devices for performing the industrial process:
receive an input selecting a machine learning model from the library of machine learning models;
insert an iconic representation of the machine learning model into a block-based representation of the control logic; and
connect the iconic representation of the machine learning model to one or more of the iconic representations of the automated devices to include the machine learning model in the control logic, wherein the control logic, when executed by a controller, instructs the automated devices of the industrial automation environment to perform the industrial process,
execute the control logic to control the industrial process, wherein the selected machine learning model optimizes a parameter of the parameters in the industrial process in response to the execution,
replace, based on outcome data received from the controlled industrial process, the selected machine learning model with a different machine learning model to modify the control logic, wherein the different machine learning model is selected from the library of machine learning models, and
execute the modified control logic, wherein the different machine learning model optimizes a different parameter of the parameters in the industrial process in response to the execution of the modified control logic.