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Open Access

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

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 611756, China.
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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 recognitionmulti-view clusteringfuzzy clustering

References

[1]
S. L. Sun, A survey of multi-view machine learning, Neural Comput. Appl., vol. 23, nos. 7&8, pp. 2031-2038, 2013.
Crossref Google Scholar
[2]
J. L. Xu, J. W. Han, and F. P. Nie, Multi-view feature learning with discriminative regularization, in Proc. 26th Int. Joint Conf. Artificial Intelligence, Melbourne, Australia, 2017, pp. 3161-3167.
Crossref
[3]
Y. T. Wang and L. H. Chen, Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources, Expert Syst. Appl., vol. 72, pp. 457-466, 2017.
Crossref Google Scholar
[4]
C. X. Yan, M. N. Luo, H. Liu, Z. H. Li, and Q. H. Zheng, Top-k multi-class SVM using multiple features, Inf. Sci., vol. 432, pp. 479-494, 2017.
Crossref Google Scholar
[5]
J. Zhao, X. J. Xie, X. Xu, and S. L. Sun, Multi-view learning overview: Recent progress and new challenges, Inf. Fusion, vol. 38, pp. 43-54, 2017.
Crossref Google Scholar
[6]
Z. Zheng, W. L. Jiang, H. S. Hu, Y. Zhu, and Y. Li, Research on rolling bearings fault diagnosis method based on EEMD morphological spectrum and kernel fuzzy C-means clustering, (in Chinese), Journal of Vibration Engineering, vol. 28, no. 2, pp. 324-330, 2015.
Google Scholar
[7]
W. L. Jiang, Z. W. Wang, Y. Zhu, Z. Zheng, and B. Zhang, Fault recognition method for rolling bearing integrating VMD denoising and FCM clustering, J. Inf. Comput. Sci., vol. 12, no. 16, pp. 5967-5975, 2015.
Crossref Google Scholar
[8]
C. Xu, D. C. Tao, and C. Xu, A survey on multi-view learning, arXiv preprint arXiv: 1304.5634, 2013.
Google Scholar
[9]
J. J. Zhao, Y. Yang, T. R. Li, and W. D. Jin, Application of empirical mode decomposition and fuzzy entropy to high-speed rail fault diagnosis, in Foundations of Intelligent Systems, Z. K. Wen and T. R. Li, eds. Springer, 2014, pp. 93-103.
Crossref
[10]
P. Yu, W. D. Jin, and N. Qin, High-speed train running gear fault feature extraction based on EEMD denoising and manifold learning, (in Chinese), Journal of the China Railway Society, vol. 38, no. 4, pp. 16-21, 2016.
Google Scholar
[11]
C. Guo, Y. Yang, H. Pan, T. R. Li, and W. D. Jin, Fault analysis of high speed train with DBN hierarchical ensemble, in Proc. 2016 Int. Joint Conf. Neural Networks, Vancouver, Canada, 2016, pp. 2552-2559.
Crossref
[12]
Y. B. Liu, Q. Qian, F. Liu, S. L. Lu, and Y. Y. Fu, Wayside acoustic fault diagnosis of train wheel bearing based on Doppler effect correction and fault-relevant information enhancement, Adv. Mech. Eng., vol. 9, no. 11, pp. 1-15, 2017.
Crossref Google Scholar
[13]
D. Y. Chen, J. H. Lin, and Y. P. Li, Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis, J. Sound Vib., vol. 424, pp. 192-207, 2018.
Crossref Google Scholar
[14]
C. Guo, Y. Yang, Y. Jiang, and T. Li, Condition analysis of high-speed train based on similarity ratio and MDBN, Comput. Sci. Eng., 2018, (in press).
Google Scholar
[15]
W. C. Xiao, Y. Yang, H. J. Wang, T. R. Li, and H. L. Xing, Semi-supervised hierarchical clustering ensemble and its application, Neurocomputing, vol. 173, pp. 1362-1376, 2016.
Crossref Google Scholar
[16]
Y. Zhao, Z. H. Guo, and J. M. Yan, Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks, J. Vib., vol. 19, no. 4, pp. 2456-2474, 2017.
Crossref Google Scholar
[17]
H. X. Hu, B. Tang, X. J. Gong, W. Wei, and H. H. Wang, Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks, IEEE Trans. Industr. Inform., vol. 13, no. 4, pp. 2106-2116, 2017.
Crossref Google Scholar
[18]
Y. Yang and H. Wang, Multi-view clustering: A survey, Big Data Mining and Analytics., vol. 1, no. 2, pp. 83-107, 2018.
Crossref Google Scholar
[19]
G. Tzortzis and A. Likas, Kernel-based weighted multi-view clustering, in Proc. 2012 IEEE 12th Int. Conf. Data Mining, Brussels, Belgium, 2012, pp. 675-684.
Crossref
[20]
H. Wang, F. P. Nie, and H. Huang, Multi-view clustering and feature learning via structured sparsity, in Proc. 30th Int. Conf. Machine Learning, Atlanta, GA, USA, 2013, pp. 352-360.
[21]
L. Du, P. Zhou, L. Shi, and H. M. Wang, Robust multiple kernel k-means using L21-norm, in Proc. 24th Int. Joint Conf. Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 3476-3482.
[22]
D. Graves and W. Pedrycz, Fuzzy C-means, Gustafson-kessel FCM, and kernel-based FCM: A comparative study, in Analysis and Design of Intelligent Systems using Soft Computing Techniques, P. Melin, O. Castillo, E. G. Ramírez, J. Kacprzyk, and W. Pedrycz, eds. Springer, 2007, pp. 140-149.
[23]
E. Trauwaert, On the meaning of Dunn’s partition coefficient for fuzzy clusters, Fuzzy Sets Syst., vol. 25, no. 2, pp. 217-242, 1988.
Crossref Google Scholar
[24]
K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 181-201, 2001.
Crossref Google Scholar
[25]
X. J. Chen, X. F. Xu, J. Z. Huang, and Y. M. Ye, TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data, IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 932-944, 2013.
Crossref Google Scholar
[26]
X. Cai, F. P. Nie, and H. Huang, Multi-view k-means clustering on big data, in Proc. 23th Int. Joint Conf. Artificial Intelligence, Beijing, China, 2013, pp. 2598-2604.
Big Data Mining and Analytics
Volume 2 Issue 1,
March 2019
Pages 1-11
DOI: 10.26599/BDMA.2018.9020027
Cite this article:
Rao Q, Yang Y, Jiang Y. Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM. Big Data Mining and Analytics, 2019, 2(1): 1-11. https://doi.org/10.26599/BDMA.2018.9020027

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Received: 09 March 2018
Accepted: 12 April 2018
Published: 15 October 2018
© The author(s) 2019
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