A sensor fault diagnosis method based on KPCA and contribution graph

Abstract

In this paper, a sensor fault diagnosis method based on KPCA and contribution graph are proposed to adapt to the nonlinear and non-Gaussian characteristics of the system. Based on the kernel function theory, this method uses SPE and T2 statistics for fault detection and contribution graph for fault location, thus completing fault diagnosis. The numerical simulation results verify that the proposed method is more effective than the traditional PCA method in detecting nonlinear faults. At the same time, the KPCA contribution map can be used to accurately locate the fault sensor, which can provide a reference value for the sensor fault diagnosis of nonlinear systems in the future.