US 12,172,323 B2Granted

Method and system for detecting anomalies in a robotic system

AssigneeABB Schweiz AG

InventorsMohak Sukhwani, Divyasheel Sharma, Sudarshan M V, Prabhat Shankar, Aravindhan Gk

Life of this patent

Estimating the term…

Loading patent…

Abstract

The present invention relates to a method and a system for detecting anomalies in a robotic system in an industrial plant. The robotic system is associated with a computing system configured to detect an anomaly in the robotic system. The computer system monitors configuration parameters of the robotic system and process parameters associated with the robotic system. Further, the computing system detects an association between at least one configuration parameter and at least one process parameter for obtaining optimal configuration parameters and optimal process parameters. The optimal configuration parameters and optimal process parameters are analyzed for detecting an anomaly. At least one parameter among the configuration parameters and the process parameters is identified causing the anomaly. Thereafter, the detected anomaly is validated, valid setpoint is estimated and the estimated valid setpoint is updated in the analytics model. The updated analytics model is subsequently used to detect anomaly accurately.

Claims10

Dependent claims

A method of detecting anomalies in a robotic system in an industrial plant, wherein the robotic system comprises at least one robot and one or more controllers of a Distributed Control System (DCS), configured to control the at least one robot to perform an operation on an object, wherein the DCS comprises an analytics model to detect anomalies in the robotic system for predefined configuration parameters of the at least one robot and predefined process parameters associated with the robotic system, the method comprises:

monitoring configuration parameters of the at least one robot and process parameters associated with the robotic system, wherein the configuration parameters are a physical parameter or variable of the at least one robot and are one of, or obtained from at least one of, one or more sensors of the DCS and a database associated with the DCS, and wherein the process parameters are a parameter or variable of the operation the robot is configured to perform;
detecting an association between at least one configuration parameter and at least one process parameter;
obtaining optimal configuration parameters and optimal process parameters based on the association, wherein the optimal configuration parameters and the optimal process parameters are analyzed for detecting an anomaly by the analytics model selected based on the obtained optimal configuration parameters and/or the obtained optimal process parameters, wherein at least one parameter is identified from the optimal configuration parameter and the optimal process parameters causing the anomaly, wherein the anomaly is validated by a plant operator;
determining an error in the analysis based on the validation, wherein the error is indicative of invalid setpoints for the at least one parameter;
estimating a valid setpoint for the at least one parameter based on the error, wherein the valid setpoint is updated in the analytics model, wherein the updated analytic model is used for detecting anomalies accurately;
and
controlling the at least one robot to perform the operation on the object based on the valid setpoint as an input to the one or more controllers.

4 more elements

A computing system for detecting anomalies in a robotic system in an industrial plant, wherein the robotic system comprises at least one robot and one or more controllers of a Distributed Control System (DCS), configured to control the at least one robot to perform an operation on an object, wherein the computing unit comprises an analytics model to detect anomalies in the robotic system for predefined configuration parameters of the at least one robot and predefined process parameters associated with the robotic system, wherein the computing unit comprises:

a processor configured to:
monitor configuration parameters of the at least one robot and process parameters associated with the robotic system, wherein the configuration parameters are a physical parameter or variable of the at least one robot and are one of, or obtained from at least one of, one or more sensors of the DCS and a database associated with the DCS, and wherein the process parameters are a parameter or variable of the operation the robot is configured to perform;
detect an association between at least one configuration parameter and at least one process parameter;
obtain optimal configuration parameters and optimal process parameters based on the association, wherein the optimal configuration parameters and the optimal process parameters are analyzed for detecting an anomaly by the analytics model selected based on the obtained optimal configuration parameters and/or the obtained optimal process parameters, wherein at least one parameter is identified from the optimal configuration parameter and the optimal process parameters causing the anomaly, wherein the anomaly is validated by a plant operator;
determine an error in the analysis based on the validation, wherein the error is indicative of invalid setpoints for the at least one parameter;
estimate a valid setpoint for the at least one parameter based on the error, wherein the valid setpoint is updated in the analytics model, wherein the updated analytic model is used for detecting anomalies accurately;
and
control the at least one robot to perform the operation on the object based on the valid setpoint as an input to the one or more controllers, and
a memory configured to store the updated analytics model, the optimal configuration parameters and the optimal process parameters.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/419,474Filed 5 Dec 2019
Priority: IN 20184105007731 Dec 2018
Published: US 2022/0118619 A121 Apr 2022
Granted: 24 Dec 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

B25J 9/1674
Program controls: characterised by safety, monitoring, diagnostic
B25J 9/161
Characterised by the control system, structure, architecture: hardware, e.g. neural networks, fuzzy logic, interfaces, processor
B25J 9/163
Characterised by the control loop: learning, adaptive, model based, rule based expert control
B25J 9/1653
Characterised by the control loop: parameters identification, estimation, stiffness, accuracy, error analysis