Unscented Kalman-filter-based simultaneous diagnostic scheme for gas-turbine gas path and sensor faults

Sensor faults can cause incorrect estimations of gas path fault amplitudes in gas turbines. In this paper, an unscented Kalman-filter (UKF)-based simultaneous diagnostic scheme for gas-turbine gas path and sensor faults is proposed. A fault detection and isolation (FDI) system based on the UKF method avoids the requirement to establish different hypothetical models for hierarchical multiple-model-based FDI. Moreover, a fault identification module based on the weighted sum of squared residuals from a bank of filters is proposed to confirm the actual fault. The corresponding fault amplitude is then estimated to adaptively update the related parameters of the fault-diagnosis system according to the actual determined fault. Finally, several simulation case studies are conducted, based on a three-shaft gas turbine. The simulation results show that when two faults coincide, the proposed scheme not only has diagnostic accuracies of 97.3% and 93% for sensor faults and gas path faults, respectively, but also estimates the fault magnitude to a high degree of accuracy.