机械故障的稀疏流形聚类与嵌入诊断方法

王江萍; 段腾飞

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

1.
西安石油大学机械工程学院, 西安 710065

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

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出版历程
- 收稿日期: 2016-04-18
- 刊出日期: 2017-10-05

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

1.
College of Mechanical Engineering, Xi'an Shiyou University, Xi'an 710065, China

摘要

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%。
- 稀疏流形聚类与嵌入 /
- 流形学习 /
- 故障诊断
Abstract: The number of neighbors in traditional manifold learning is fixed, but it is unbefitting in fault detection. In order to solve this problem, a new fault detection method using sparse manifold clustering and embedding (SMCE) is presented in this paper. SMCE extracts the low dimension manifold structure from observation space with high dimension and nonlinearity by solving sparse optimization problem and finding neighbors automatically. The parameter selection of neighbors is converted to select penalty coefficient of sparse optimization problem. By means of SMCE, the vibration feature of bearing and gear is extracted and supervised and unsupervised fault detection is achieved. The experiment illustrates that this method is better to extract the internal structure of fault signal and to detect mechanical fault. The detection accuracy with spectral clustering and SVM is higher than 98%.
- Sparse manifold clustering and embedding /
- manifold learning /
- fault detection

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参考文献(18)

[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