|Table of Contents|

Rolling bearing fault diagnosis method based on wavelet packet entropy and clustering analysis

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2013年04期
Page:
517-
Research Field:
Publishing date:

Info

Title:
Rolling bearing fault diagnosis method based on wavelet packet entropy and clustering analysis
Author(s):
Yang QingSun BaicongZhu MeichenYang QingchuanLiu Nian
School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China
Keywords:
wavelet packet entropy subtractive clustering rolling bearing fault diagnosis K-means clustering
PACS:
TH165.3
DOI:
-
Abstract:
In order to improve the fault diagnosis accuracy of rolling bearing vibration signals,an ensemble approach based on wavelet packet entropy and clustering analysis is presented here.The method of wavelet packet is used to decompose rolling bearing vibration signals into three-layer,and extract the energy characteristics.The vibration signal energy distribution is used as the probability distribution to do the information entropy calculations and extract the vibration signal characteristics.To detect faults,combined with subtractive clustering,the K-means clustering method of optimizing initial cluster centers by the principle of highest density index is proposed.To test the effectiveness of the proposed method,the actual bearing data of rolling bearing with different fault diameters are provided in the experiment.The results show that the proposed approach avoids the sensibility of traditional K-means clustering to initial cluster centers and its result can be used as a basis for rolling bearing fault diagnosis.

References:

[1] 汤宝平,蒋永华,张详春.基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法[J].机械工程学报,2010,46(5):37-48.
Tang Baoping,Jiang Yonghua,Zhang Xiangchun.Feature extraction method of rolling bearing fault based on singular value decomposition-morphology filter and empirical mode decomposition[J].Journal of Mechanical Engineering,2010,46(5):37-48.
[2]郝研,王太勇,万剑,等.分形盒维数抗噪研究及其在故障诊断中的应用[J].仪器仪表学报,2011,32(3):540-545.
Hao Yan,Wang Taiyong,Wan Jian,et al.Research on fractal box dimension anti-noise performance and its application in fault diagnosis[J].Chinese Journal of Scientific Instrument,2011,32(3):540-545.
[3]王树亮,王东,冯珍,等.基于小波包-神经网络故障诊断系统研究[J].南京理工大学学报,2004,28(4):356-359.
Wang Shuliang,Wang Dong,Feng Zhen,et al.Study of fault diagnosis system based on wavelet packet-neural network[J].Journal of Nanjing University of Science and Technology,2004,28(4):356-359.
[4]吴丹,顾学迈.一种新的基于支持向量机的自动调制识别方案[J].南京理工大学学报,2006,30(5):569-573.
Wu Dan,Gu Xuemai.Novel scheme of automatic modulation recognition based on SVM[J].Journal of Nanjing University of Science and Technology,2006,30(5):569-573.
[5]冯志刚,王祁,徐涛,等.基于小波包和支持向量机的传感器故障诊断方法[J].南京理工大学学报,2008,32(5):609-614.
Feng Zhigang,Wang Qi,Xu Tao,et al.Sensor fault diagnosis based on wavelet packet and support vector machines[J].Journal of Nanjing University of Science and Technology,2008,32(5):609-614.
[6]Yiakopoulos C T,Gryllias K C,Antoniadis I A.Rolling element bearing fault detection in industrial environments based on a K-means clustering approach[J].Expert Systems with Applications,2011,38(3):2888-2911.
[7]王艳景,乔晓艳,李鹏,等.基于小波包熵和支持向量机的运动想象任务分类研究[J].仪器仪表学报.2010,31(12):2729-2735.
Wang Yanjing,Qiao Xiaoyan,Li Peng,et al.Classification of motor imagery task based on wavelet packet entropy and support vector machines[J].Chinese Journal of Scientific Instrument,2010,31(12):2729-2735.
[8]张荣标,胡海燕,冯友兵.基于小波熵的微弱信号检测方法研究[J].仪器仪表学报,2007,28(11):2078-2084.
Zhang Rongbiao,Hu Haiyan,Fong Youbing.Study on weak signal detection method based on wavelet entropy[J].Chinese Journal of Scientific Instrument,2007,28(11):2078-2084.
[9]潘天红,薛振框,李少远.基于减法聚类的多模型在线辨识算法[J].自动化学报,2009,35(2):220-224.
Pan Tianhong,Xue Zhenkuang,Li Shaoyuan.An online multi-model identification algorithm based on subtractive clustering[J].Acta Automatica Sinica,2009,35(2):220-224.
[10]Jain A K.Data clustering:50 years beyond K-means[J].Pattern Recognition Letters,2010,31(8):651-666.
[11]Erisoglu M,Calis N,Sakallioglu S.A new algorithm for initial cluster centers in k-means algorithm[J].Pattern Recognition Letters,2011,32(14):1701-1705.
[12]Gao Jinxin,Hitchcock D B.James-Stein shrinkage to improve k-means cluster analysis[J].Computational Statistics and Data Analysis,2010,54(9):2113-2127.
[13]Bearing Data Center.Seeded fault test data.http://csegroups.case.edu/bearingdatacenter/home,2012-06-20.

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Last Update: 2013-08-31