Abstract
Gas-path analysis method has been widespread applied to gas turbine engine health control and has become one of the key techniques in favor of condition-oriented maintenance strategy. Theoretically, gas-path analysis method (especially nonlinear gas-path analysis) can easily quantify gas-path component degradations. However, when the number of components within engine is large which highly expands the dimension of fault coefficient matrix, maybe leading to strong smearing effect (i.e. predicted degradations are located almost in all component health parameters, although some of them are not really degraded), the degraded components may not be accurately identified. In order to improve the robustness of gas turbine gas-path fault diagnosis, a hybrid gas-path analysis approach integrating gray relation algorithm into gas-path analysis method has been proposed in this study. The gray relation algorithm and gas-path analysis method approach includes two steps. First, the faulty components are recognized and isolated by gray relation algorithm, which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the gas-path analysis. The fault classification analyses and case studies have shown that the confidence level of the fault classification can reach more than 95%, when single and multiple components are degraded, and the predicted degradations are almost same as that of implanted fault patterns.
Highlights
Gas turbine engine always runs at poor working conditions with high-temperature, high-pressure, and high mechanical and thermal stress, and the performance of its gas-path components degrades gradually, leading to various serious faults
The faulty components are recognized and isolated by gray relation algorithm (GRA) which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the gas-path analysis method (GPA)
In order to improve the robustness of gas turbine gas-path fault diagnosis, a hybrid GPA approach (GRA-GPA) integrating gray relation algorithm (GRA) into GPA method has been proposed to isolate and quantify gas-path component degradations
Summary
Gas turbine engine always runs at poor working conditions with high-temperature, high-pressure, and high mechanical and thermal stress, and the performance of its gas-path components degrades gradually, leading to various serious faults. The reason why GPA methods have been widely applied to monitor gas turbine engine health status is that GPA can quantify gas-path component degradations by thermodynamic performance model which relates gas-path measurements (e.g. temperatures, pressures, fuel flow rate, and shaft rotational speeds) with the fundamental component performance parameters (e.g. mass flow rate, pressure ratio, and isentropic efficiency).[19] when the number of components involved in fault diagnosis is large which highly expands the dimension of fault coefficient matrix, maybe leading to strong smearing effect, the degraded components may not be accurately identified. A new diagnostic approach has been proposed in order to effectively isolate the degraded components and accurately quantify the fault degradations with a hybrid of gray relation algorithm and gaspath analysis method (GRA-GPA). The faulty components are recognized and isolated by GRA which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the GPA
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