Fully Decomposed Singular Value and Fixed Dictionary Extreme Learning Machine for Bogie Fault Diagnosis

Abstract

As an essential part in the rail train, the bogie plays an important role in the safety of the train operation. However, the fluctuant wheel-rail connection, as well as the structure and complex operating environment of the bogie always lead to low signal-to-noise ratio condition and complicated wheel-rail dynamic coupling relationship. The existing fault diagnosis methods can hardly perform well in this scenario. Concerning this issue, a novel feature extraction method named fully decomposed singular value (FdSV) is proposed in this paper. FdSV can decompose singular value characteristics of signals completely and increase the divergence of features to extract weak fault features effectively. Then, inspired by the theory of compressed perception and Hierarchy-ELM, a fixed dictionary extreme learning machine (FD-ELM) is also proposed for fault identification. This method calculates the weight matrix by formulas without randomization and removes the bias matrix. Therefore, it can easily discover the internal laws of data and improve the running speed and accuracy rapidly. Finally, the proposed algorithms have been verified by actual bogie data collected from bogies under low SNR and variable working conditions. Compared with SVD, the FdSV features are 1%-6% higher in testing accuracies. The accuracies of FD-ELM are 2-20% higher than the conventional ELM, H-ELM and SVM.