Paper Info
Title
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.
Year
DOI
Venue
2022
10.1109/TITS.2021.3089181
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Bogie,fault diagnosis,fully-decomposed singular values,singular value decomposition,fixed dictionary extreme learning machine,variable conditions
Journal
23
Issue
ISSN
Citations 
8
1524-9050
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
Yakun Zuo100.68
Ning Wang200.34
Limin Jia300.68
Huiyue Zhang400.34
Zhipeng Wang500.68
Yong Qin6164.52