Model-based Gas Path Diagnostics For Heavy-duty Gas Turbine

The unscheduled maintenance or outages of gas turbines due to degradation cannot only induce great additional costs and lost revenues but also influence the safety andreliability of gas turbines. Gas path diagnostics and prognosis can be used to assessengine conditions in real-time and predict the failure time in the future, so the predictivemaintenance actions can be performed. The benefits of predictive maintenance actionsare improved usability, safety and reliability as well as reduced life cycle costs.Therefore, the study of gas path diagnostics has great theoretical significance andpractical value. This dissertation focuses on the research on the model-based gas pathdiagnostic method, and develops model-based gas path diagnostic system of heavy-dutygas turbine.Firstly, an adaptive generic object-oriented model for gas path diagnostics ofheavy-duty gas turbine is established. The model simulation results match with the fielddata, which confirms the accuracy of the model. The state space model, linear andnonlinear adaptive gas path diagnostic model, and Extended Kalman Filter(EKF) basedadaptive gas path diagnostic model of heavy-duty gas turbine are developed to analyzeand diagnose the gas path fault.Secondly, the assessment of gas path diagnostic accuracy and optimalmeasurement selections method for model-based gas path diagnostics is studied. Anovel degree of observability analysis method for measurement selections of gas pathdiagnostics is developed. The states with high degree of observability and themeasurement sets with high overall degree of observability result in high estimationaccuracy in gas path diagnostics. Using the proposed method, the influence of themeasurement noise, the overdetermined measurement, the gas turbine nonlinearity andthe advanced measurement such as turbine inlet temperature on degree of observabilityand gas path diagnostic accuracy are analyzed. The optimal measurement selections areconducted for dynamic and stable model-based gas path diagnostics.Thirdly, the tracking of gas path fault based on adaptive EKF is studied. Accordingto the drawbacks of the EKF, adaptive gas path diagnostics using the strong trackingfilter(STF) is proposed. The proposed method can track both the abrupt fault andgradual fault accurately, which overcomes the drawbacks of the EKF. Based on the analysis of the principle of the EKF, the algorithm of the STF is improved to increasethe robustness against the gas path fault magnitude, initialization value of filter andmeasurement noise.Finally, the underdetermined gas path diagnostic problem of model-based gas pathdiagnostic method is studied. A new gas path diagnostic method based on SparseBayesian Learning favoring sparse solutions for abrupt fault events is proposed, whichcan be used to diagnose the component fault and sensor bias fault together for abruptfault events. The analysis of the a variety gas path diagnostics results demonstrate thecapability, accuracy and superior over other methods.