LMD能量熵和SVM相结合的滚动轴承故障诊断

徐乐; 邢邦圣; 郎超男; 高钦武

doi:10.13433/j.cnki.1003-8728.2017.0615

LMD能量熵和SVM相结合的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.2017.0615

1.
江苏师范大学, 江苏徐州 221116

基金项目:

江苏省"六大人才高峰"高层次人才项目（2012-ZBZZ-038）、江苏省普通高校研究生科研创新计划项目（SJLX_0656）、江苏师范大学博士科研支持项目（14XLR033）及江苏师范大学研究生科研创新计划重点项目（2015YZD018）资助

详细信息

作者简介:
徐乐(1990-),硕士,研究方向为旋转机械故障诊断、设备运行状态监测与控制,1183116563@qq.com

通讯作者:
邢邦圣(联系人),教授,博士,xbs138@jsnu.edu.cn

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- 文章访问数: 210
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- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2015-09-17
- 刊出日期: 2017-06-05

Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM

1.
Jiangsu Normal University, Jiangsu Xuzhou 221116, China

摘要

摘要: 为实现小样本情况下对滚动轴承进行故障检测和分析，提出了基于局部均值分解（LMD）的能量熵和支持向量机（SVM）相结合的滚动轴承故障诊断方法。利用LMD信号处理方法将滚动轴承振动信号分解成有限个乘积函数（PF）分量，通过计算PF分量的能量熵进行故障特征提取，然后将提取的特征输入到SVM分类器中进行训练及测试，最终实现对滚动轴承的故障诊断。实验数据显示，在仅有少量样本条件下，LMD能量熵和SVM相结合的方法能够精确地对滚动轴承的故障类型进行识别和分类，这表明该方法对滚动轴承故障诊断的有效性。
- 滚动轴承 /
- 故障诊断 /
- 局部均值分解 /
- 能量熵 /
- 支持向量机
Abstract: To achieve the fault detection and failure analysis of rolling bearing for small samples, a rolling bearing fault diagnosis method is proposed based on the local mean decomposition (LMD) energy entropy and the support vector machines (SVM). In this method, the rolling bearing vibration signals are decomposed into several production functions (PF) by using the LMD signal processing method. Then the energy entropy of the PF components for fault feature extraction is calculated and the features are input into the SVM classifiers for training and testing. Finally, the fault diagnosis of rolling bearing is performed. The experimental results show that the proposed method can be used effectively to identify and classify the type of rolling bearing fault accuratelyfor small samples.
- rolling bearing /
- fault diagnosis /
- ocal mean decomposition /
- energy entropy /
- support vector machines

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参考文献(15)

[1]	Saruhan H, Sandemir S, Çiçek A, et al. Vibration analysis of rolling element bearings defects[J]. Journal of Applied Research and Technology, 2014,12(3):384-395
[2]	唐先广,郭瑜,丁彦春,等.基于短时傅里叶变换和独立分量分析的滚动轴承包络分析[J].机械强度,2012,34(1):1-5 Tang X G, Guo Y, Ding Y C, et al. Application of rolling element bearing envelope analysis based on short time Fourier transition and independent components analysis[J]. Journal of Mechanical Strength, 2012,34(1):1-5 (in Chinese)
[3]	石林锁,张亚洲,米文鹏.基于WVD的谱峭度法在轴承故障诊断中的应用[J].振动、测试与诊断,2011,31(1):27-31 Shi L S, Zhang Y Z, Mi W P. Application of Wigner-Ville distribution based spectral kurtosis algorithm to fault diagnosis of rolling bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2011,31(1):27-31 (in Chinese)
[4]	Kankar P K, Sharma S C, Harsha S P. Rolling element bearing fault diagnosis using wavelet transform[J]. Neurocomputing, 2011,74(10):1638-1645
[5]	靳同红,莫正波,郑德亮,等.时频分析技术及应用研究[J].机械科学与技术,2009,28(1):75-78 Jin T H, Mo Z B, Zheng D L, et al. A review of time-frequency analysis and its application[J]. Mechanical Science and Technology for Aerospace Engineering, 2009,28(1):75-78 (in Chinese)
[6]	Smith J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005,2(5):443-454
[7]	Ma J, Wu J D, Yuan X Y. The fault diagnosis of the rolling bearing based on the LMD and time-frequency analysis[J]. International Journal of Control and Automation, 2013,6(4):357-376
[8]	聂鹏,高辉,陈彦海,等.局部均值分解在刀具故障诊断中的应用[J].北京理工大学学报,2012,32(11):1125-1128,1133 Nie P, Gao H, Chen Y H, et al. Application of local mean decomposition in tool fault diagnosis[J]. Transactions of Beijing Institute of Technology, 2012,32(11):1125-1128,1133 (in Chinese)
[9]	Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995
[10]	董绍江,汤宝平,张焱.基于非广延小波特征尺度熵和支持向量机的轴承状态识别[J].振动与冲击,2012,31(15):50-54 Dong S J, Tang B P, Zhang Y. Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine[J]. Journal of Vibration and Shock, 2012,31(15):50-54 (in Chinese)
[11]	张超,陈建军.基于LMD近似熵和支持向量机的轴承故障诊断[J].机械科学与技术,2012,31(9):1539-1542,1548 Zhang C, Chen J J. Diagnosing faults of bearings with LMD approximate entropy and SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2012,31(9):1539-1542,1548 (in Chinese)
[12]	楼红伟,马振书,孙华刚,等.基于PSO-SVM的齿轮箱故障诊断研究[J].机械科学与技术,2014,33(9):1364-1367 Lou H W, Ma Z S, Sun H G, et al. Research of gear box fault diagnosis based on PSO-SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(9):1364-1367 (in Chinese)
[13]	梁世杰,韩维,康小伟,等.局域均值分解中PF获取方法的改进[J].航空计算技术,2012,42(4):24-27 Liang S J, Han W, Kang X W, et al. Improvement of PF definition of local mean decomposition[J]. Aeronautical Computing Technique, 2012,42(4):24-27 (in Chinese)
[14]	鞠萍华,秦树人,赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击,2011,30(2):1-4,23 Ju P H, Qin S R, Zhao L. Energy operator demodulating approach based on LMD and its application in extracting characteristics of a fault signal[J]. Journal of Vibration and Shock, 2011,30(2):1-4,23 (in Chinese)
[15]	程军圣,于德介,杨宇.基于内禀模态奇异值分解和支持向量机的故障诊断方法[J].自动化学报,2006,32(3):475-480 Cheng J S, Yu D J, Yang Y. Fault diagnosis approach based on intrinsic mode singular value decomposition and support vector machines[J]. Acta Automatica Sinica, 2006,32(3):475-480 (in Chinese)