噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究

王建国,陈帅,张超

振动与冲击 ›› 2017, Vol. 36 ›› Issue (5) : 72-78.

PDF(1358 KB)
PDF(1358 KB)
振动与冲击 ›› 2017, Vol. 36 ›› Issue (5) : 72-78.
论文

噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究

  • 王建国,陈帅,张超
作者信息 +

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

  • WANG Jianguo,CHEN Shuai,ZHANG Chao
Author information +
文章历史 +

摘要

针对轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本,提出基于噪声参数最优的总体局部均值分解(ensemble local mean decomposition, ELMD)与最小二乘支持向量机(least squares support vector machine, LS-SVM)相结合的轴承故障诊断方法。首先对轴承振动信号进行噪声参数最优ELMD分解并得到一系列窄带乘积函数(product function, PF),然后计算各PF分量能量以构造能量特征向量,最后将高维能量特征向量作为最小二乘支持向量机的输入来识别轴承故障类型。通过对轴承故障振动信号分析,结果表明噪声参数最优ELMD方法能有效地抑制模态混叠,与LS-SVM结合可以准确地识别轴承的工作状态和故障类型。

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. 

关键词

最优噪声参数  / 总体局部均值分解  / 能量特征向量  / 最小二乘支持向量机  / 故障诊断

Key words

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

引用本文

导出引用
王建国,陈帅,张超 . 噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J]. 振动与冲击, 2017, 36(5): 72-78
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

参考文献

[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.

PDF(1358 KB)

Accesses

Citation

Detail

段落导航
相关文章

/