Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier

doi:10.3901/JME.2018.09.168

Abstract

Abstract: The vibration signals resulting from rolling bearings are non-linear and non-stationary, an approach for the fault diagnosis of railway rolling bearings using the multi-scale IMF permutation entropy and SA-SVM classifier is proposed. The signal is first denoised using wavelet de-noising (WD) as a pre-filter, which improves the subsequent decomposition into a number of intrinsic mode functions (IMFs) using ensemble empirical mode decompose (EEMD). Secondly, the multi-scale IMF permutation entropy are extracted as feature parameters. Finally, the extracted features are given input to SA-SVM for an automated fault diagnosis procedure. The results demonstrate its effectiveness for railway rolling bearings fault diagnosis. The fault diagnosis system has high application value in practical engineering.

Key words: EEMD, fault diagnosis, multi-scale IMF permutation entropy, railway rolling bearing, SA-SVM

CLC Number:

TH133
U279

YAO Dechen, YANG Jianwei, CHENG Xiaoqing, WANG Xing. Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier[J]. Journal of Mechanical Engineering, 2018, 54(9): 168-176.

References

[1] HE Q, WANG J, HU F, et al. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement[J]. Journal of Sound and Vibration, 2013, 332(21):5635-5649.
[2] 唐贵基,邓飞跃,何玉灵. 基于自适应多尺度自互补Top-Hat变换的轴承故障增强检测[J]. 机械工程学报, 2015, 51(19):93-100. TANG Guiji, DENG Feiyue, HE Yuling. Enhanced detection of bearing faults based on adaptive multi-scale self-complementary Top-Hat transformation[J]. Journal of Mechanical Engineering, 2015, 51(19):93-100.
[3] 隋文涛,张丹, WILSON Wang. 基于EMD和MKD的滚动轴承故障诊断方法[J]. 振动与冲击, 2015, 34(9):55-59. SUI Wentao, ZHANG Dan, WILSON Wang. Fault diagnosis of rolling element bearings based on EMD and MKD[J]. Journal of Vibration and Shock, 2015, 34(9):55-59.
[4] 苏文胜,王奉涛,张志新. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击, 2010, 29(3):18-21. SU Wensheng, WANG Fengtao, ZHANG Zhixin. Application of EMD denoising and spectral kurtosis in fault early diagnosis of element bearing[J]. Journal of Vibration and Shock, 2010, 29(3):18-21.
[5] 张志刚,石晓辉,陈哲明. 基于改进EMD与滑动峰态算法的滚动轴承故障特征提取[J]. 振动与冲击, 2012, 31(22):80-83. ZHANG Zhigang, SHI Xiaohui, CHEN Zheming. Fault feature extraction of rolling element bearing based on improved EMD and sliding kurtosis algorithm[J]. Journal of Vibration and Shock, 2012, 31(22):80-83.
[6] 徐卓飞,刘凯,张海燕,等. 基于经验模态分解和主元分析的滚动轴承故障诊断方法研究[J]. 振动与冲击, 2014, 33(23):133-139. XU Zhuofei, LIU Kai, ZHANG Haiyan, et al. A fault diagnosis method for rolling bearings based on empirical mode decomposition and principal component analysis[J]. Journal of Vibration and Shock, 2014, 33(23):133-139.
[7] WU Z H, HUANG N E. Ensemble empirical mode decomposition:A noise-assisted data analysis method[J]. Advanced in Adaptive Data Analysis, 2009, 1(1):1-41.
[8] BANDT C, POMPER B. Permutation entropy:A natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17):1-4.
[9] YAN R Q, LIU Y B, GAO R X. Permutation entropy:A nonlinear statistical measure for status characterization of rotary machine[J]. Mechanical System and Signal Processing, 2012, 29:474-484.
[10] 冯辅周,饶国强,司爱威. 基于排列熵和神经网络的滚动轴承异常检测与诊断[J]. 噪声与振动控制, 2013, 33(3):212-217. FENG Fuzhou, RAO Guoqiang, SI Aiwei. Abnormality detection and diagnosis of rolling bearing based on permutation entropy and neural network[J]. Noise and Vibration Control, 2013, 33(3):212-217.
[11] 冯辅周,饶国强,司爱威. 排列熵算法研究及其在振动信号突变检测中的应用[J]. 振动工程学报,2012,25(2):221-224. FENG Fuzhou, RAO Guoqiang, SI Aiwei. Research and application of the arithmetic of PE in testing the sudden change of vibration signal[J]. Journal of Vibration Engineering, 2012, 25(2):221-224.
[12] 郑近德,程军圣,杨宇. 多尺度排列熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2013, 24(19):2641-2646. ZHENG Jinde, CHENG Junsheng, YANG Yu. Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis[J]. China Mechanical Engineering, 2013, 24(19):2641-2646.
[13] 武兵,林健,熊晓燕. 基于支持向量回归的多参数设备故障预测方法[J]. 振动、测试与诊断, 2012, 32(5):791-795. WU Bing, LIN Jian, XIONG Xiaoyan. Method of mechanical equipment fault prognosis based on multi-parameter support vector regression[J]. Journal of Vibration, Measurement & Diagnosis, 2012, 32(5):791-795.
[14] 胡玉霞,张红涛. 基于模拟退火算法-支持向量机的储粮害虫识别分类[J]. 农业机械学报, 2008, 39(9):108-111. HU Yuxia, ZHANG Hongtao. Recognition of the stored-grain pests based on simulated annealing algorithm and support vector machine[J]. Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(9):108-111.
[15] 张超,陈建军. EEMD方法和EMD方法抗模态混叠对比研究[J]. 振动与冲击, 2010, 29(S):87-90. ZHANG Chao, CHEN Jianjun. Contrast of ensemble empirical mode decomposition and empirical mode decomposition in modal mixture[J]. Journal of Vibration and Shock, 2010, 29(S):87-90.
[16] 李昌林,孔凡让,黄伟国. 基于EEMD和Laplace小波的滚动轴承故障诊断[J]. 振动与冲击, 2014, 33(3):63-69. LI Changlin, KONG Fanrang, HUANG Weiguo. Rolling bearing fault diagnosis based on EEMD and laplace wavelet[J]. Journal of Vibration and Shock, 2014, 33(3):63-69.
[17] WU Z H, HUANG N E. Ensemble empirical mode decomposition:A noise assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1:1-41.
[18] 雷亚国,孔德同,李乃鹏,等. 自适应总体平均经验模态分解及其在行星齿轮箱故障检测中的应用[J]. 机械工程学报, 2014, 50(3):64-70. LEI Yaguo, KONG Detong, LI Naipeng, et al. Adaptive ensemble empirical mode decomposition and its application to fault detection of planetary gearboxes[J]. Journal of Mechanical Engineering, 2014, 50(3):64-70.
[19] 任静波,孙根正,陈冰,等. 基于多尺度排列熵的铣削颤振在线监测方法[J]. 机械工程学报, 2015, 51(9):206-212. REN Jingbo, SUN Genzheng, CHEN Bing, et al. Multi-scale permutation entropy based on-line milling chatter detection method[J]. Journal of Mechanical Engineering, 2015, 51(9):206-212.
[20] 纪华, 马伏龙. 模拟退火算法与支持向量机在机械故障诊断中的应用[J]. 宁夏大学学报, 2014, 35(2):141-143. JI Hua, MA Fulong. The application on the simulated annealing algorithm and the support vector machines in mechanical fault diagnosis[J]. Journal of Ningxia University, 2014, 35(2):141-143.