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Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.


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Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM

Show Author's information Qi RaoYan Yang( )Yongquan Jiang
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 611756, China.

Abstract

Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.

Keywords:

High-Speed Train (HST), condition recognition, multi-view clustering, fuzzy clustering
Received: 09 March 2018 Accepted: 12 April 2018 Published: 15 October 2018 Issue date: March 2019
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Publication history

Received: 09 March 2018
Accepted: 12 April 2018
Published: 15 October 2018
Issue date: March 2019

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© The author(s) 2019

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 61572407 and 61134002).

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