Condition recognition of high-speed train based on multi-view weighted clustering ensemble

doi:10.3969/j.issn.0253-2778.2018.01.005

Abstract

Abstract: With the rapid development of China's high-speed train industry, some safety problems arising from the high-speed train operation are attracting more attention. Since the monitoring signals of the high-speed trains collected by sensors are nonlinear and non-stationary, it is difficult to identify the fault conditions of high-speed train. Therefore, in this paper, a multi-view clustering ensemble model based on weighted non-negative matrix factorization (WNMF) is proposed to it. Firstly, the vibration signals are analyzed the frequency domain, time-frequency domain and time domain. And the multi-views are obtained by extracting the eigenvector from the four aspects of the vibration signal, which are fast Fourier transform, wavelet packet energy, approximate entropy and fuzzy entropy of empirical mode decomposition, and the mechanical statistical characteristics. And then the clustering result of each view is obtained by the K-means. Secondly, two kinds of weight of the views are generated respectively by the contribution and the similarity of the clustering partitions. Finally, the output results of multiple clustering and the weights are combined for WNMF to ensemble. The experimental results show that the model can better identify fault conditions of high-speed trains.

Key words: high-speed train, condition recognition, multi-view, clustering ensemble, feature extraction

CLC Number:

TP181

RAO Qi, YANG Yan*, TENG Fei. Condition recognition of high-speed train based on multi-view weighted clustering ensemble[J]. Journal of University of Science and Technology of China, 2018, 48(1): 35-41.

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