Constraint Violation Root-Cause Analysis in Process Controllers
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Solution Overview
Problem
Model-based industrial process controllers face challenges in maintaining optimal performance over time due to factors like inaccurate models, misconfiguration, and operator actions, leading to significant reductions in benefits, potentially up to fifty percent or more.
Innovation Solution
An apparatus and method for automated identification and diagnosis of constraint violations, which involves obtaining data on process variables, analyzing controller and process behaviors, and generating graphical displays to identify probable causes of constraint violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model-based controllers are used to control industrial processes, then control performance and efficiency are improved, but performance degradation occurs over time due to inaccurate models, misconfiguration, or operator actions
Solution Approach 1:
The system implements automated feedback by continuously monitoring controller performance metrics and constraint violations, then using this information to diagnose root causes and recommend corrective actions. The feedback loop includes performance tracking, anomaly detection, cause analysis, and remediation guidance to restore optimal controller performance.
Solution Approach 2:
The system performs preliminary diagnostic actions by analyzing historical data and identifying potential performance degradation causes before they significantly impact controller performance. It proactively detects constraint violations and predicts potential issues, allowing operators to take corrective action before performance deteriorates further.
2Loss of time
If automated diagnosis systems are implemented to identify constraint violations, then troubleshooting time and costs are reduced, but system complexity and implementation requirements increase
Solution Approach 1:
The system enables self-service diagnosis by automatically collecting performance data, identifying constraint violations, analyzing root causes, and generating diagnostic reports without requiring external expert intervention. The controller essentially diagnoses its own performance issues, reducing the need for external troubleshooting resources.
Solution Approach 2:
The diagnostic system is designed to be universally applicable across different model-based controllers and industrial processes. It handles multiple types of constraint violations, performance metrics, and diagnostic scenarios through a unified platform, reducing implementation complexity through standardization.
Data Source
AI summary
A method includes obtaining data identifying values of one or more process variables associated with an industrial process controller and identifying one or more constraint violations using the data. The method also includes, for each identified constraint violation, analyzing a behavior of the controller, a behavior of an industrial process being controlled, and how the controller was being used by at least one operator for a period of time. At least part of the period of time is prior to the identified constraint violation. The method further includes generating a graphical display based on the analysis, where the graphical display identifies one or more probable causes for at least one of the one or more constraint violations.