Application of noise parametric optimization with ELMD and LS-SVM in bearing fault diagnosis

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (5) : 72-78.

WANG Jianguo,CHEN Shuai,ZHANG Chao

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Abstract

Aiming at non-stationary features of bearing vibration signals and difficulties to obtain a large number of fault samples in reality,a method of bearing fault diagnosis based on noise parametric optimization with ensemble local mean decomposition(ELMD) and least squares support vector machine(LS-SVM) was proposed.Firstly,a bearing vibration signal was decomposed into a series of narrow band product functions(PFs) using the optimal noise parameters ELMD method.Then,the energy of each PF was calculated to construct energy feature vectors.Finally,the high-dimensional energy feature vectors were taken as inputs of LS-SVM to identify bearing fault types.The results of bearing fault vibration signals analysis indicated that the optimal noise parameters ELMD method can suppress mode-mixing effectively and this approach combined with LS-SVM can identify operating conditions and fault types of bearings correctly.

Key words

optimal noise parameters / ELMD / energy feature vectors / LS-SVM / fault diagnosis

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WANG Jianguo,CHEN Shuai,ZHANG Chao. Application of noise parametric optimization with ELMD and LS-SVM in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2017, 36(5): 72-78

References

[1] 何正嘉，袁静，訾艳阳，著.机械故障诊断的内积变换原理与应用[M]. 北京：科学出版社，2012.
HE Zheng-jia, YUAN Jing, ZI Yan-yang. Inner product principle of mechanical fault diagnosis and application[M]. Beijing: Science Press, 2012.
[2] 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.
[3] 张超，陈建军. 基于LMD和Lempel-Ziv指标的滚动轴承故障损伤程度研究[J]. 振动与冲击，2012，31(16)：77-82.
ZHANG Chao, CHEN Jian-jun. Fault severity assessment for rolling element bearings based on LMD and Lempel-Ziv index[J]. Journal of Vibration and Shock, 2012, 31(16): 77-82.
[4] 王衍学，何正嘉，訾艳阳，等. 基于LMD的时频分析方法及其机械故障诊断应用研究[J]. 振动与冲击，2012，31(9)：9-12.
WANG Yan-xue, HE Zheng-jia, ZI Yan-yang, et al. Several key issues of local mean decomposition method used in mechanical fault diagnosis[J]. Journal of Vibration and Shock, 2012, 31(9):9-12.
[5] 杨川，于德介，徐亚军. 基于EMD与BP神经网络的汽车关门声品质预测[J]. 汽车工程，2013，35(5)：457-461.
YANG Chuan, YU De-jie, XU Ya-jun. Sound quality prediction for vehicle door-slamming noise based on empirical mode decomposition and back propagation neural network[J]. Automotive Engineering, 2013, 35(5): 457-461.
[6] 马文朋，张俊红，马梁，等. 改进的经验模式分解在机械故障诊断中的应用[J]. 振动、测试与诊断，2015, 35(4)：637-644.
MA Wen-peng, ZHANG Jun-hong, MA Liang, et al. Applications of improved empirical mode decomposition in machinery fault diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2015, 35(4): 637-644.
[7] 张亢，程军圣，杨宇. 基于有理样条函数的局部均值分解方法及其应用[J]. 振动工程学报，2011，24(1)：96-103.
ZHANG Kang, CHENG Jun-sheng, YANG Yu. The local mean decomposition method based on rational spline and its application[J]. Journal of Vibration Engineering, 2011, 24(1): 96-103.
[8] 吴小涛，杨锰，袁晓辉，等. 基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断[J]. 振动与冲击，2015，34(2)：38-44.
WU Xiao-tao, YANG Meng, YUAN Xiao-hui, et al. Bearing fault diagnosis using EEMD and improved morphological filtering method based on kurtosis criterion[J]. Journal of Vibration and Shock, 2015, 34(2): 38-44.
[9] 位秀雷，林瑞霖，刘树勇，等. 小波-SG-EEMD混合算法及混沌去噪应用研究[J]. 振动与冲击，2015，34(17)：100-104.
WEI Xiu-lei, LIN Rui-lin, LIU Shu-yong, et al. Hybrid wavelet-SG-EEMD algorithm and its application in chaotic de-noising[J] . Journal of Vibration and Shock, 2015, 34(17)：100-104.
[10] YANG Yu, CHENG Jun-sheng, ZHANG Kang. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J]. Measurement, 2012, 45(3): 561-570.
[11] 廖星智，万舟，熊新. 基于ELMD与LS-SVM的滚动轴承故障诊断方法[J]. 化工学报，2013，64(12)：4667-4673.
LIAO Xing-zhi, WAN Zhou, XIONG Xin. Fault diagnosis method of rolling bearing based on ensemble local mean decomposition and least squares support vector machine[J]. Journal of Chemical Industry and Engineering, 2013, 64(12): 4667-4673.
[12] LUO Xian-jin, HUANG Xiu-mei. Fault diagnosis of wind turbine based on ELMD and FCM[J]. Open Mechanical Engineering Journal, 2014, 8: 716-720.
[13] SUN Jie-di, XIAO Qi-yang, WEN Jiang-tao, et al. Natural gas leak location with K-L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function[J]. Journal of Sound and Vibration, 2015, 347: 232-245.
[14] Zhang J, Yan R Q, Gao R X, et al. Performance enhancement of ensemble empirical mode decomposition[J]. Mechanical Systems and Signal Processing, 2010, 24(7): 2104-2123.
[15] Zvokelj M, Zupan S, Prebil I. Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis[J]. Mechanical Systems and Signal Processing, 2010, 24(4): 1049-1067.
[16] Zvokelj M, Zupan S, Prebil I. Nonlinear multivariate and multiscale monitoring and signal denoising strategy using Kernel principal component analysis combined with ensemble empirical mode decomposition method[J].Mechanical Systems and Signal Processing, 2011, 25(7): 2631–2653.
[17] Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010,2(2): 135–156.
[18] Chang K M, Liu S H. Gaussian noise filtering from ECG by Wiener filter and ensemble empirical mode decomposition[J]. Journal of Signal Processing, 2011,64(2): 249–264.
[19] Niazy R K, Beckmann C F, Brady J M, et al. Performance evaluation of ensemble empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2009,1(2): 231-242.
[20] Langone R ,Alzate C, Ketelaere B D, et al. LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines[J]. Engineering Applications of Artificial Intelligence, 2015, 37: 268-278.
[21] Guo W, Tse P W. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals[J]. Journal of Sound and Vibration, 2013,332(2): 423-441.