公开(公告)号：US20250164972A1

公开(公告)日：2025-05-22

申请号：US19027692

申请日：2025-01-17

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio ,  John J. Hagerbaumer

IPC: G05B19/418

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 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 analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

公开(公告)号：US12235627B2

公开(公告)日：2025-02-25

申请号：US17484720

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B19/418

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 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 analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

公开(公告)号：US20230099239A1

公开(公告)日：2023-03-30

申请号：US17484364

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Jeffrey S. Sperling ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B13/02 ,  G06T7/00

Abstract: Various embodiments of the present technology generally relate to integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises a screen capture component configured to capture images of a human-machine interface in an industrial automation environment, wherein the one or more images include at least one visual depiction of data collected from an industrial device. The system further comprises an input component configured to provide the images to a machine learning model configured to analyze an operating condition of the industrial device. The system also comprises a determination component configured to, based on an output of the machine learning model, identify a current operating condition of the industrial device.

公开(公告)号：US12200000B2

公开(公告)日：2025-01-14

申请号：US17870516

申请日：2022-07-21

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: H04L9/40

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component monitors an integrated design application and generates feature vectors that represent operations of the integrated design application. The security component supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the integrated design application. When anomalous behavior is detected in the operations of the integrated design application, the security component generates and transfers an alert that characterizes the anomalous behavior.

公开(公告)号：US12130611B2

公开(公告)日：2024-10-29

申请号：US17484691

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Jeffrey S. Sperling ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G06N20/00 ,  G05B19/418 ,  G06N20/20

CPC classification number: G05B19/41845 ,  G05B19/4183 ,  G06N20/20

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

公开(公告)号：US12085910B2

公开(公告)日：2024-09-10

申请号：US17484657

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B19/042

CPC classification number: G05B19/0426 ,  G05B2219/23077

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.

公开(公告)号：US20240019853A1

公开(公告)日：2024-01-18

申请号：US17862898

申请日：2022-07-12

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B19/418

CPC classification number: G05B19/4185 ,  G05B19/4183

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component generates feature vectors that represents inputs and outputs to a data pipeline and supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the data pipeline. When anomalous behavior is detected in the operations of the data pipeline, the security component generates and transfers an alert that characterizes the anomalous behavior.

公开(公告)号：US20230100333A1

公开(公告)日：2023-03-30

申请号：US17484720

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B19/418

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 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 analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

公开(公告)号：US20250138494A1

公开(公告)日：2025-05-01

申请号：US19008390

申请日：2025-01-02

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B13/04 ,  G05B13/02

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.

公开(公告)号：US12242233B2

公开(公告)日：2025-03-04

申请号：US17484461

申请日：2021-09-24

Applicant: Rockwell Automation Technologies, Inc.

Inventor： Jordan C. Reynolds ,  John J. Hagerbaumer ,  Troy W. Mahr ,  Thomas K. Jacobsen ,  Giancarlo Scaturchio

IPC: G05B13/04 ,  G05B13/02

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.