Gas Control Valve Trip Prediction via Prognostic Indicators
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Solution Overview
Problem
Existing systems fail to predict gas turbine trips due to gas control valve failures, leading to unwarranted shutdowns that result in revenue loss and reduced turbine component life, as they do not effectively monitor and diagnose issues such as mechanical actuator leakages, mechanical jamming, and servo issues in real time.
Innovation Solution
A method and system for predicting gas turbine trips by collecting raw operational data, extracting features using domain knowledge, applying a rule set based on apriori probability, and determining a fused belief of failure to generate a prognostic indicator for impending trips, enabling proactive detection and prevention of GCV system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing servo system monitoring and diagnostics are used, then the system can detect component failures, but it cannot predict trips due to gas control valve failures
Solution Approach 1:
The system performs preliminary analysis of operational data to generate prognostic indicators before actual failures occur. By continuously monitoring GCV operational parameters and comparing them against learned failure patterns, the system predicts potential trips in advance, enabling proactive maintenance before the failure actually happens.
Solution Approach 2:
The system implements a feedback mechanism where operational data from GCV systems is continuously collected, analyzed, and used to update prediction models. The prognostic indicators generated are fed back to operators and maintenance systems, creating a closed-loop information flow that improves prediction accuracy over time and enables continuous monitoring of system health.
2Loss of energy
If gas turbine trips are prevented through better monitoring, then revenue loss is reduced, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary prognostic indicator layer between the raw operational data and the trip decision-making process. This intermediary layer analyzes operational parameters, compares them against failure patterns, and generates predictive information that helps distinguish between normal variations and actual failure precursors, reducing unwarranted trips without requiring direct intervention in the control system.
Solution Approach 2:
The system replaces complex mechanical monitoring approaches with data-driven prognostic algorithms. Instead of relying on purely mechanical sensors and switches, the system uses computational analysis of operational data to predict failures, substituting mechanical complexity with information processing that can be implemented through software and standard computing hardware.
3Measurement precision
If real-time monitoring of gas control valve parameters is implemented, then trip prediction accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant operational parameters from the vast amount of available data for prognostic analysis. By identifying and focusing on key indicators such as GCV position, pressure differentials, and flow rates that are most predictive of failures, the system achieves high detection accuracy while minimizing the computational resources required to process and analyze the data.
Data Source
AI summary
Systems and methods for prediction of gas turbine trips due to component failures such as electro-hydraulic valve (gas control valve) system failures. Exemplary embodiments include prediction of gas turbine trips due to component failures, the method including collecting raw gas turbine operational data and using the raw gas turbine operational data to generate a prognostic indicator for the prediction of a turbine trip due to the failed gas control valves.