摘要：AbstractA methodology is proposed to improve the robustification of the Unscented Kalman Filtering by adjusting the measurement noise covariance matrix by introducing two adaptive factors. The combined effect of the two adaptive factors helps to identify and filter the outliers more prominently. Improvement in the proposed robust UKF algorithm is established by analyzing the percentage reduction of accumulation error for different conditions with the larger measurement noise covariance. In addition, a method of fault identification of gas turbine engines is developed by the proposed robust UKF approach in the presence of system and measurement noises. The proposed fault identification method deals with the analysis of the residual signature of different states along with the cost function of the derived algorithm. Different component and sensor anomalies are introduced in the nonlinear dynamic model of the gas turbine engine to produce the faulty situations. The deviations in performances are compared with the nominal behavior to obtain the residual signature. A comparative study is performed to identify the faulty scenarios.
