旋转机械故障的拉普拉斯支持向量机诊断方法

摘要/Abstract

摘要：

在旋转机械故障智能诊断中，收集大量的样本比较容易，而要对所有的样本进行类别标记却较为困难。针对这一问题，提出了一种基于拉普拉斯支持向量机的旋转机械故障智能诊断方法。滚动轴承故障诊断实例表明，有标记样本的数量较少时，与仅使用有标记样本进行学习的支持向量机相比，基于拉普拉斯支持向量机的诊断方法利用大量的无标记样本进行辅助学习，可以显著提高故障诊断的正确率。

关键词: 故障诊断, 滚动轴承, 支持向量机, 半监督学习, 流形学习

Abstract:

In the intelligent fault diagnosis of rotating machinery, collecting a large number of data was relatively easy, but giving all collected data a label was often difficult. Aiming at this situation, an intelligent fault diagnosis approach for rotating machinery was proposed based on Laplacian support vector machines(LapSVM). The diagnosis example of rolling bearings shows that when the number of labeled data is limited, compared with the SVM that uses only labeled data for learning, the fault diagnosis approach based on LapSVM can improve the accuracy of fault diagnosis significantly by using a large amount of unlabeled data together with labeled data for learning.

Key words: fault diagnosis, rolling bearing, support vector machine(SVM), semi-supervised learning, manifold learning

中图分类号:

TH17

郝腾飞, 陈果. 旋转机械故障的拉普拉斯支持向量机诊断方法[J]. 中国机械工程, 2016, 27(01): 73-78.

Hao Tengfei, Chen Guo. Fault Diagnosis of Rotating Machinery Based on Laplacian Support Vector Machines[J]. China Mechanical Engineering, 2016, 27(01): 73-78.

参考文献

[1]徐启华，师军. 基于支持向量机的航空发动机故障诊断[J]. 航空动力学报，2005，20(2)：298-302.
Xu Qihua，Shi Jun. Aero-engine Fault Diagnosis Based on Support Vector Machine[J]. Journal of Aerospace Power，2005，20(2)：298-302.
[2]王太勇，何慧龙，王国峰，等. 基于经验模式分解和最小二乘支持矢量机的滚动轴承故障诊断[J]. 机械工程学报，2007，43(4)：88-92.
Wang Taiyong，He Huilong，Wang Guofeng，et al. Rolling Bearings Fault Diagnosis Based on Empirical Mode Decomposition and Least Square Support Vector Machine[J]. Chinese Journal of Mechanical Engineering，2007，43(4)：88-92.
[3]Hu Qiao，He Zhengjia，Zhang Zhousuo，et al. Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVMs Ensemble[J]. Mechanical Systems and Signal Processing，2007，21(2)：688-705.
[4]袁胜发，褚福磊. 支持向量机及其在机械故障诊断中的应用[J]. 振动与冲击，2007，26(11)：29-35.
Yuan Shengfa，Chu Fulei. Support Vector Machines and Its Application in Machine Fault Diagnosis[J]. Journal of Vibration and Shock，2007，26(11)：29-35.
[5]Widodo A，Yang B S. Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis[J]. Mechanical Systems and Signal Processing，2007，21(6)：2560-2574.
[6]Chapelle O，Schlkopf B，Zien A. Semi-supervised Learning[M]. Cambridge，MA，USA：MIT Press，2006.
[7]Zhu Xiaojin，Goldberg A B. Introduction to Semi-supervised Learning[M]. San Rafael：Morgan & Claypool Publishers，2009.
[8]Zhang Zhenyue，Wang Jing，Zha Hongyuan. Adaptive Manifold Learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2012，34(2)：253-265.
[9]蒋全胜，贾民平，胡建中，等. 基于拉普拉斯特征映射的故障模式识别方法[J]. 系统仿真学报，2008，20(20)：5710-5713.
Jiang Quansheng，Jia Minping，Hu Jianzhong，et al. Method of Fault Pattern Recognition Based on Laplacian Eigenmaps[J]. Journal of System Simulation，2008，20(20)：5710-5713.
[10]Li Benwei，Zhang Yun. Supervised Locally Linear Embedding Projection (SLLEP) for Machinery Fault Diagnosis[J]. Mechanical Systems and Signal Processing，2011，25(8)：3125-3134.
[11]Belkin M，Niyogi P，Sindhwani V. Manifold Regularization：A Geometric Framework for Learning from Labeled and Unlabeled Examples[J]. Journal of Machine Learning Research，2006，7(11)：2399-2434.
[12]Melacci S，Belkin M. Laplacian Support Vector Machines Trained in the Primal[J]. Journal of Machine Learning Research，2011，12(3)：1149-1184.
[13]The Case Western Reserve University Bearing Data Center. Bearing Data Center Fault Test Data[EB/OL]. [2012-03-01].http://www.eecs.cwru.edu/laboratory/bearing/.
[14]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[J]. Proceedings of the Royal Society of London. Series A：Mathematical，Physical and Engineering Sciences，1998，454(1971)：903-995.
[15]Yang Yu，Yu Dejie，Cheng Junsheng. A Roller Bearing Fault Diagnosis Method Based on EMD Energy Entropy and ANN[J]. Journal of Sound and Vibration，2006，294(1/2)：269-277.
[16]Rifkin R，Klautau A. In Defense of One-vs-All Classification[J]. Journal of Machine Learning Research，2004，5(1)：101-141.