ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

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

  • School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.01.005
  • Received Date: 29 May 2017
  • Rev Recd Date: 22 June 2017
  • Publish Date: 31 January 2018
    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.

    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.
    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    GUO C, YANG Y, PAN H, et al. Fault analysis of high speed train with DBN hierarchical ensembles[C]// Proceedings of the International Joint Conference on Neural Networks. Vancouver：IEEE Press, 2016: 2552-2559.
    [2]
    HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]// Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 1998, 454(1971): 903-995.
    [3]
    李智敏, 苟先太, 秦娜, 等. 高速列车振动监测信号的频率特征[J]. 仪表技术与传感器, 2015, (5): 99-103.
    [4]
    ZHAO J, YANG Y, LI T, et al. Application of Empirical Mode Decomposition and Fuzzy Entropy to High-Speed Rail Fault Diagnosis[M]. Foundations of Intelligent Systems. Springer Berlin Heidelberg, 2014: 93-103.
    [5]
    刘林艳, 金炜东, 余志斌. 基于小波分析的高速列车车体运行状态估计[J]. 计算机应用研究, 2013, 30(10): 2948-2950.
    [6]
    XU C, TAO D, XU C. A survey on multi-view learning[EB/OL]. [2017-05-10] https://arxiv.org/pdf/1304.5634v1.pdf.
    [7]
    ROKACH L. A survey of clustering algorithms[J]. Data Mining and Knowledge Discovery Handbook, 2009, 16(3): 269-298.
    [8]
    STREHL A, GHOSH J. Cluster ensembles: A knowledge reuse framework for combining partitionings[C]// Proceedings of the 8th National Conference on Artificial Intelligence. Edmonton, Canada: ACM Press, 2002: 93-99.
    [9]
    GREENE D, CUNNINGHAM P. A matrix factorization approach for integrating multiple data views[C]// Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2009: 423-438.
    [10]
    黄采伦, 樊晓平, 陈春阳, 等. 基于小波系数提取及离散余弦包络分析的机车牵引齿轮故障诊断方法[J]. 铁道学报, 2008, 30(2): 98-102.
    [11]
    张乾. 基于振动信号的轴承状态监测和故障诊断方法研究[D]. 长沙: 中南大学, 2012.
    [12]
    SRA S, DHILLON I S. Nonnegative matrix approximation: Algorithms and applications[EB/OL]. [2017-04-28]http://www.cs.utexas.edu/ftp/techreports/tr06-27.pdf.
    [13]
    李乐, 章毓晋. 非负矩阵分解算法综述[J]. 电子学报, 2008, 36(4): 737-743.
    [14]
    杨燕, 靳蕃, KAMEL M. 聚类有效性评价综述[J]. 计算机应用研究, 2008, 25(6): 1630-1632.
    [15]
    CAI X, NIE F, HUANG H. Multi-view k-means clustering on big data[C]// Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing: IEEE Press2013: 2598-2604.
    [16]
    XIA R, PAN Y, DU L, et al. Robust multi-view spectral clustering via low-rank and sparse decomposition[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: ACM Press, 2014: 2149-2155.
    [17]
    KUMAR A, RAI P, DAUMé H. Co-regularized multi-view spectral clustering[C]// Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada, Spain: ACM Press, 2011: 1413-1421.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [1]
    GUO C, YANG Y, PAN H, et al. Fault analysis of high speed train with DBN hierarchical ensembles[C]// Proceedings of the International Joint Conference on Neural Networks. Vancouver：IEEE Press, 2016: 2552-2559.
    [2]
    HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]// Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 1998, 454(1971): 903-995.
    [3]
    李智敏, 苟先太, 秦娜, 等. 高速列车振动监测信号的频率特征[J]. 仪表技术与传感器, 2015, (5): 99-103.
    [4]
    ZHAO J, YANG Y, LI T, et al. Application of Empirical Mode Decomposition and Fuzzy Entropy to High-Speed Rail Fault Diagnosis[M]. Foundations of Intelligent Systems. Springer Berlin Heidelberg, 2014: 93-103.
    [5]
    刘林艳, 金炜东, 余志斌. 基于小波分析的高速列车车体运行状态估计[J]. 计算机应用研究, 2013, 30(10): 2948-2950.
    [6]
    XU C, TAO D, XU C. A survey on multi-view learning[EB/OL]. [2017-05-10] https://arxiv.org/pdf/1304.5634v1.pdf.
    [7]
    ROKACH L. A survey of clustering algorithms[J]. Data Mining and Knowledge Discovery Handbook, 2009, 16(3): 269-298.
    [8]
    STREHL A, GHOSH J. Cluster ensembles: A knowledge reuse framework for combining partitionings[C]// Proceedings of the 8th National Conference on Artificial Intelligence. Edmonton, Canada: ACM Press, 2002: 93-99.
    [9]
    GREENE D, CUNNINGHAM P. A matrix factorization approach for integrating multiple data views[C]// Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2009: 423-438.
    [10]
    黄采伦, 樊晓平, 陈春阳, 等. 基于小波系数提取及离散余弦包络分析的机车牵引齿轮故障诊断方法[J]. 铁道学报, 2008, 30(2): 98-102.
    [11]
    张乾. 基于振动信号的轴承状态监测和故障诊断方法研究[D]. 长沙: 中南大学, 2012.
    [12]
    SRA S, DHILLON I S. Nonnegative matrix approximation: Algorithms and applications[EB/OL]. [2017-04-28]http://www.cs.utexas.edu/ftp/techreports/tr06-27.pdf.
    [13]
    李乐, 章毓晋. 非负矩阵分解算法综述[J]. 电子学报, 2008, 36(4): 737-743.
    [14]
    杨燕, 靳蕃, KAMEL M. 聚类有效性评价综述[J]. 计算机应用研究, 2008, 25(6): 1630-1632.
    [15]
    CAI X, NIE F, HUANG H. Multi-view k-means clustering on big data[C]// Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing: IEEE Press2013: 2598-2604.
    [16]
    XIA R, PAN Y, DU L, et al. Robust multi-view spectral clustering via low-rank and sparse decomposition[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: ACM Press, 2014: 2149-2155.
    [17]
    KUMAR A, RAI P, DAUMé H. Co-regularized multi-view spectral clustering[C]// Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada, Spain: ACM Press, 2011: 1413-1421.
    Recommended articles
    TrendMD
    Volume 48 Issue 1 page: 35-41
    Cover

    Article Metrics

    Article views (534) PDF downloads(213)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return