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机械故障的稀疏流形聚类与嵌入诊断方法

王江萍 段腾飞

王江萍, 段腾飞. 机械故障的稀疏流形聚类与嵌入诊断方法[J]. 机械科学与技术, 2017, 36(10): 1582-1588. doi: 10.13433/j.cnki.1003-8728.2017.1016
引用本文: 王江萍, 段腾飞. 机械故障的稀疏流形聚类与嵌入诊断方法[J]. 机械科学与技术, 2017, 36(10): 1582-1588. doi: 10.13433/j.cnki.1003-8728.2017.1016
Wang Jiangping, Duan Tengfei. The Method of Machinery Fault Detection using Sparse Manifold Clustering and Embedding[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(10): 1582-1588. doi: 10.13433/j.cnki.1003-8728.2017.1016
Citation: Wang Jiangping, Duan Tengfei. The Method of Machinery Fault Detection using Sparse Manifold Clustering and Embedding[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(10): 1582-1588. doi: 10.13433/j.cnki.1003-8728.2017.1016

机械故障的稀疏流形聚类与嵌入诊断方法

doi: 10.13433/j.cnki.1003-8728.2017.1016
基金项目: 

西安石油大学全日制硕士研究生优秀学位论文培育项目(2015YP140407)资助

详细信息
    作者简介:

    王江萍(1959-),教授,硕士,研究方向为工程检测与故障诊断技术,jpwang@xsyu.edu.cn

The Method of Machinery Fault Detection using Sparse Manifold Clustering and Embedding

  • 摘要: 传统流形学习算法中邻域尺寸是固定的,在故障诊断中并不恰当。本文中提出了一种基于新型流形学习算法稀疏流形聚类与嵌入(SMCE)的机械故障诊断方法来解决这个问题。SMCE通过求解稀疏优化问题自动确定邻域的大小,将传统流形学习中邻域尺寸选择变为优化问题的惩罚系数选择,进而从高维非线性观测数据中提取流形结构。利用SMCE从轴承和齿轮振动信号中提取特征进行诊断,实验表明,所提方法可以较好的提取故障信号内在的几何结构,应用无监督的谱聚类和有监督的支持向量机进行诊断准确率均高于98%。
  • [1] 王江萍.机械设备故障诊断技术及应用[M].西安:西北工业大学出版社,2001:1-3 Wang J P. The fault diagnosis technology of mechanical equipment and its application[M]. Xi'an:Northwestern Polytechnical University Press, 2001:1-3(in Chinese)
    [2] Seung H S, Lee D D. The manifold ways of perception[J]. Science, 2000,290(5500):2268-2269
    [3] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000,290(5500):2323-2326
    [4] Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000,290(5500):2319-2323
    [5] 蒋全胜.基于流形学习的机械故障诊断理论与方法研究[D].南京:东南大学,2009:24,49 Jiang Q S. Study on theory and methodology of machinery fault diagnosis based on manifold learning[D]. Nanjing:Southeast University, 2009:24,49(in Chinese)
    [6] Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering[C]//Advances in Neural Information Processing Systems 14. Cambridge, MA:MIT press, 2002:585-591
    [7] Zhang Z Y, Zha H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. Journal of Shanghai University, 2004,8(4):406-424
    [8] 向丹,葛爽.一种基于小波包样本熵和流形学习的故障特征提取模型[J].振动与冲击,2014,33(11):1-5 Xiang D, Ge S. A model of fault feature extraction based on wavelet packet sample entropy and manifold learning[J]. Journal of Vibration and Shock, 2014,33(11):1-5(in Chinese)
    [9] 王冠伟,张春霞,庄健,等.流形学习在机械故障诊断中的应用研究[J].工程数学学报,2012,29(4):593-599 Wang G W, Zhang C X, Zhuang J, et al. An investigation of applying manifold learning to diagnose machinery faults[J]. Chinese Journal of Engineering Mathematics, 2012,29(4):593-599(in Chinese)
    [10] Wang G W, Zhuang J, Yu D H. Research and application of manifold learning to fault diagnosis of reciprocating compressor[C]//Proceedings of 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. Yantai, China:IEEE, 2010:2652-2656
    [11] Elhamifar E, Vidal R. Sparse manifold clustering and embedding[C]//Advances in neural Information Processing Systems 24. New York:Curran Associates Inc., 2011:55-63
    [12] Ng A Y, Jordan M I, Weiss Y. On spectral clustering:analysis and an algorithm[C]//Advances in Neural Information Processing Systems. Cambridge:MIT Press, 2002,2:849-856
    [13] 汤宝平,马婧华.多准则融合敏感特征选择和自适应邻域的流形学习故障诊断[J].仪器仪表学报,2014,35(11):2415-2422 Tang B P, Ma J H. Manifold learning method for fault diagnosis based on sensitive feature selection with multi-criteria evaluation sequences and adaptive neighborhood[J]. Chinese Journal of Scientific Instrument, 2014,35(11):2415-2422(in Chinese)
    [14] 黄宏臣,韩振南,张倩倩,等.基于拉普拉斯特征映射的滚动轴承故障识别[J].振动与冲击,2015,34(5):128-134,144 Huang H C, Han Z N, Zhang Q Q, et al. Method of fault diagnosis for rolling bearings based on Laplacian eigenmap[J]. Journal of Vibration and Shock, 2015,34(5):128-134,144(in Chinese)
    [15] 马维金,张琳,张纪平,等.基于流形学习算法的齿轮变速箱故障特征提取[J].机械传动,2015,(8):111-114 Ma W J, Zhang L, Zhang J P, et al. Fault feature extraction of the gearbox based on manifold learning algorithm[J]. Journal of Mechanical Transmission, 2015,(8):111-114(in Chinese)
    [16] 赵冲冲,廖明夫,于潇.基于支持向量机的旋转机械故障诊断[J].振动、测试与诊断,2006,26(1):53-57 Zhao C C, Liao M F, Yu X. Application of support vecter machine to fault diagnosis of rotation machinery[J]. Journal of Vibration, Measurement & Diagnosis, 2006,26(1):53-57(in Chinese)
    [17] Zhang L J, Xu J W, Yang J H, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008,22(3):597-610
    [18] Yang H Y, Mathew J, Ma L. Fault diagnosis of rolling element bearings using basis pursuit[J]. Mechanical Systems and Signal Processing, 2005,19(2):341-356
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出版历程
  • 收稿日期:  2016-04-18
  • 刊出日期:  2017-10-05

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