A Multi-mode Incipient Sensor Fault Detection and Diagnosis Method for Electrical Traction Systems

Abstract

This paper proposes a data-driven sensor fault detection and diagnosis (FDD) method for electrical traction systems. Considering their switched characteristics, electrical traction systems can be regarded as switched systems. A mixture non-Gaussian data set will be formed, which can be firstly divided into six different operation modes, and principal component analysis (PCA) is then used for feature extraction in each mode. For two fault indicators in principal and residual subspaces, their probability density functions (PDFs) are estimated and used to determine reasonable thresholds for FDD. The proposed methodology extends the application of multivariate statistical technology to electrical traction systems. It can be applied easily and effectively without requirements on system parameters, and can deal with incipient sensor faults in traction system. Experiments with several different types of incipient sensor faults are conducted, which can demonstrate the effectiveness of the proposed method.