Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms

In this paper, we first study a generalized canonical correlation analysis (CCA)-based fault detection (FD) method aiming at maximizing the fault detectability under an acceptable false alarm rate. More specifically, two residual signals are generated for detecting of faults in input and output subspaces, respectively. The minimum covariances of the two residual signals are achieved by taking the correlation between input and output into account. Considering the limited application scope of the generalized CCA due to the Gaussian assumption on the process noises, an FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains. The achieved results show that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized CCA-based FD method.