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

  • Hongtian Chen
  • Bin JiangEmail author
  • Ningyun Lu
Regular Papers Control Theory and Applications


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.


Electrical traction system fault detection and diagnosis (FDD) incipient sensor fault multi-mode non-Gaussian signal 


Rn× m

The set of all n×m real matrix


Matrix with N observations from m sensors


The i-th mode


Loading matrix in principal subspace

Loading matrix in residual subspace


Eigenvalue matrix in principal subspace


Eigenvalue matrix in residual subspace


The number of retained principal components


Probability density function (PDF) of x

\({J_{{T^2}}}\), JSPE

Thresholds for T2 and SPE


Kernel function of x


Chi-square distribution


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingP. R. China
  2. 2.Jiangsu Key Laboratory of Internet of Things and Control Technologies (Nanjing University of Aeronautics and Astronautics)NanjingP. R. China

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