Fault diagnosis method of rolling bearing based on VMD-MPE -KPCA feature extraction mixed with MRVM

Abstract

Abstract: In view of the nonlinear, non-stationary and strong interference characteristics of rolling bearing vibration signals in strong noise environment, which leads to difficulty of fault feature extraction and low accuracy of fault diagnosis, this paper proposes a rolling bearing fault diagnosis method on the basis of combination of variational mode decomposition (VMD)-multi-scale permutation entropy (MPE)- kernel principal component analysis (KPCA) and multi-class relevant vector machine (MRVM). In this method, high-dimensional fault features of rolling bearing vibration signals are extracted first by VMD-MPE, and then the extracted fault features are visually reduced dimensionally by KPCA. Finally, the dimensionally-reduced fault features are input into MRVM which can realize different sample probability output for rolling bearing fault diagnosis. The effectiveness of the proposed method is verified by a set of rolling bearing fault data published by Western Reserve University in the United States. The results show that the proposed rolling bearing fault diagnosis method based on “VMD-MPE-KPCA” feature extraction and MRVM may extract and identify rolling bearing fault features effectively. If compared with those fault diagnosis methods reported in related literature, the fault identification accuracy of the proposed hybrid intelligent fault diagnosis method reaches up to 99.18%.

Key words: rolling bearing, fault diagnosis, VMD, MPE, MRVM

CLC Number:

TP181
TH13

CHEN Peng, ZHAO Xiao-qiang, ZHU Qi-xian. Fault diagnosis method of rolling bearing based on VMD-MPE -KPCA feature extraction mixed with MRVM[J]. Journal of Lanzhou University of Technology, 2020, 46(5): 92-99.

References

[1] LI Y,XU M,WANG R,et al.A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy[J].Journal of Sound and Vibration,2016,360:277-299.
[2] PARK C S,CHOI Y C,KIM Y H.Early fault detection in automotive ball bearings using the minimum variance cepstrum[J].Mechanical Systems & Signal Processing,2013,38(2):534-548.
[3] 李舜酩,郭海东,李殿荣.振动信号处理方法综述[J].仪器仪表学报,2013,34(8):1907-1915.
[4] FENG Z,LIANG M,CHU F.Recent advances in time-frequency analysis methods for machinery fault diagnosis:A review with application examples[J].Mechanical Systems & Signal Processing,2013,38(1):165-205.
[5] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings A,1998,454(1971):903-995.
[6] XU Guanlei,WANG Xiaotong,Xu Xiaogang,et al.Improved EMD for the analysis of FM signals[J].Mechanical Systems and Signal Processing,2012,33:181-196.
[7] WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method [J].Advances in Adaptive Data Analysis,2008,1(1):1-41.
[8] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[9] 唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-648.
[10] 向丹,岑健.基于EMD熵特征融合的滚动轴承故障诊断方法[J].航空动力学报,2015,30(5):1149-1155.
[11] 陈东宁,张运东,姚成,等.基于变分模态分解和多尺度排列熵的故障诊断[J].计算机集成制造系统,2017,23(12):2604-2612.
[12] 郑近德,代俊习,朱小龙,等.基于改进多尺度模糊熵的滚动轴承故障诊断方法[J].振动、测试与诊断,2018,38(5):929-934.
[13] BANDT C,POMPE B.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,2002,88(17):174102.
[14] AZIZ W,ARIF M.Multiscale permutation entropy of physiological time series[C]//9thInternational Multitopic Conference.[S.l.]:IEEE,2005:1-6.
[15] 刘涛,马转霞,杜楠.多尺度排列熵在涡旋压缩机故障诊断中的应用[J].兰州理工大学学报,2018,44(1):42-46.
[16] SUN W,CHEN J,LI J.Decision tree and PCA-based fault diagnosis of rotating machinery[J].Noise & Vibration Worldwide,2007,21(3):1300-1317.
[17] 欧璐,于德介.基于监督拉普拉斯分值和主元分析的滚动轴承故障诊断[J].机械工程学报,2014,50(5):88-94.
[18] LI Meng.The application of PCA and SVM in rolling bearing fault diagnosis[J].Advanced Materials Research,2012,430-432:1163-1166.
[19] JIA G,YUAN S,TANG C.Fault diagnosis of roller bearing based on PCA and multi-class support vectormachine[J].Ifip Advances in Information & Communication Technology,2010,347:198-205.
[20] SCHMÖLKOPF B,SMOLA A J,MÜLER K R.Kernel principal component analysis[C]//International Conference on Artificial Neural Networks.Berlin,Heidelberg:Springer,1999:327-352.
[21] JIANG L L,DENG Z Q,TANG S W.KPCA denoising and its application in machinery fault diagnosis[J].Applied Mechanics and Materials,2011,103:274-278.
[22] ZHANG Y,ZUO H,BAI F.Classification of fault location and performance degradation of a roller bearing[J].Measurement,2013,46(3):1178-1189.
[23] 王长林,陈鸿宝,林玮,等.SVM模式识别技术及在机械故障诊断中的应用进展[J].桂林电子科技大学学报,2009,29(3):256-259.
[24] TIPPING M E.The relevance vector machine[J].Advances in Neural Information Processing Systems,1999,12(3):652-658.
[25] PSORAKIS I,DAMOULAS T,GIROLAMI M A.Multiclass relevance vector machines:sparsity and accuracy[J].IEEE Transactions on Neural Networks,2010,21(10):1588-1598.
[26] Case Western Reserve University.Bearing data center[DB/OL].[2019-05-20].http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[27] 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):2641-2645.
[28] 郑小霞,周国旺,任浩翰,等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击,2017(22):22-28.
[29] ZHAO L Y,WANG Lei,YAN R Q.Rolling bearing fault diagnosis based on wavelet packet decomposition and multi-scale permutation entropy[J].Entropy,2015,17(12):6447-6461.