Sensor Fault Detection and Identification Method with KPCA in the Process of Aero-Engine Ground Testing

This paper focuses on Kernel Principal Component Analysis (KPCA) in order to solve the problem of sensor fault detection and identification without linear relationship between sensors. After samples are projected into high-dimensional space, new Principal Component Analysis (PCA) model is established in the kernel principal component eigenvector space. By the contrast with SPE, Hotelling T2 is an appropriate parameter to detect sensor fault because it is more sensitive to sensor failure. The contribution of different sensor to Hotelling T2 is utilized to identify sensor fault for the faulty sensor’s contribution is large than others significantly. Finally, the proposed method is illustrated by the sensors of oil-providing system in the process of aero-engine ground testing. Experiment results show the applicability and effectiveness of the proposed method.