Incipient fault diagnosis of rolling bearing based on VMD with parameters optimized

PDF(1247 KB)

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (23) : 38-46.

WANG Hengdi1, DENG Sier1, YANG Jianxi1, LIAO Hui2

Author information +

History +

Abstract

Aiming at the problem of incipient fault features being difficult to extract in original vibration signals of rolling bearing, an incipient fault diagnosis method of rolling bearing based on variational mode decomposition (VMD) with parameters optimized was proposed.Firstly, Beetle antennae search (BAS) algorithm was used to search the optimal parameter combination of VMD algorithm.The reciprocals of kurtosis values of intrinsic mode functions (IMFs) obtained with VMD were taken as fitness functions in the search process. The number of IMFs and the quadratic penalty factor of VMD algorithm were set up according to the obtained results after search.Then, the bearing vibration signal was decomposed using VMD algorithm with parameters optimized, and the optimal IMF component was chosen with the kurtosis criterion.Hilbert envelope demodulation calculation was done for the optimal IMF component to gain its envelope spectrum.This envelope spectrum could reveal more obvious fault impulse features to realize incipient fault diagnosis of rolling bearing.The results were compared with those obtained using EMD, VMD with fixed parameters and tests results showed that the proposed method can more effectively extract incipient fault features of rolling bearing.

Key words

rolling bearing / incipient fault diagnosis / variational mode decomposition (VMD) / BAS algorithm / envelope spectrum

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Hengdi1, DENG Sier1, YANG Jianxi1, LIAO Hui2. Incipient fault diagnosis of rolling bearing based on VMD with parameters optimized[J]. Journal of Vibration and Shock, 2020, 39(23): 38-46

References

[1] 黄之初，张嘉凡. 滚动轴承故障脉冲信息提取及诊断：一种盲解卷积方法[J]. 振动与冲击，2006,25(3):150-154.
HUANG Zhichu, ZHANG Jiafan. Faulty impulse signals extraction and diagnosis of rolling element bearing: A blind deconvolution method [J]. Journal of Vibration and Shock, 2006,25(3):150-154.
[2] 郭宝良，段志善，郑建校，等. 振动机械滚动轴承单点单蚀故障诊断研究[J]. 振动工程学报，2012,25(5):610-618.
GUO Baoliang, DUAN Zhishan, ZHENG Jianxiao, et al. Fault diagnosis of single-point pitting corrosion for rolling bearing of vibrating machine [J]. Journal of Vibration Engineering, 2012,25(5):610-618.
[3] N.E.Huang，Z.Shen，S.R.Long. The Empirical Mode Decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis [J].Proc of the Royal of London, Series A,1998,454(12):903-995.
[4] N.E.Huang，Wu M C，Long S R,et al. A confidence limit for the empirical mode decompsition and the Hilbert spectrum analysis [J]. Proc of the Royal of London, Series A,2003,459(2037):2317-2345.
[5] Yaguo Lei, Jing Lin, Zhengjia He, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2013,35: 108–126.
[6] Konstantin Dragomiretskiy, Dominique Zosso. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014,62(3): 531–544.
[7] Wenxian Yang, Zhike Peng, Kexiang Wei, et al. Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring [J]. IET Renewable Power Generation, 2016,11(4): 443-452.
[8] 王新，闫文源. 基于变分模态分解和SVM的滚动轴承故障诊断[J]. 振动与冲击，2017,36(18):252-256.
WANG Xin, YAN Wenyuan. Fault diagnosis of roller bearings based on the variational mode decomposition and SVM [J]. Journal of Vibration and Shock, 2017,36(18):252-256.
[9] 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方法[J]. 振动与冲击，2017, 36(22):22-28.
Zheng Xiaoxia, Zhou Guowang, Ren Haohan, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy [J]. Journal of Vibration and Shock, 2017, 36(22):22-28.
[10] 李加福，唐文彦，张晓琳，等. 壳段厚度激光检测信号的变分模态分解去噪[J]. 光学精密工程, 2017, 25(8) :2173-2181.
Li Jiafu, Tang Wenyan, Zhang Xiaolin, et al. Adaptive denoising for laser detection signal of shell thickness based on variational mode decomposition [J]. Optics and Precision Engineering. 2017,25(8): 2173-2181.
[11] Jijian Lian, Zhuo Liu, Haijun Wang, et al. Adaptive variational mode decomposition method for signal processing based on mode characteristic [J]. Mechanical Systems and Signal Processing, 2018,107: 53–77.
[12] Yan Liu, Jindong Wang, Ying Li, et al. Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve [J]. Journal of Vibroengineering, 2017,19(8): 6007-6020.
[13] Xin Zhang, Qiang Miao, Heng Zhang, et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery [J]. Mechanical Systems and Signal Processing, 2018,108: 58–72.
[14] 唐贵基，王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, 49(5):73-81.
Tang Guiji, Wang Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing [J]. Journal of Xi’An Jiaotong University, 2015,49(5): 73-81.
[15] Xiangyuan Jiang, Shuai Li. BAS: Beetle antennae search algorithm for optimization problems [J]. International Journal of Robotics and Control, 2018, 1(1): 1-5. https://doi.org/10.5430/ijrc.v1n1p1.
[16] Yan Zhang, Baoping Tang, Xin Xiao. Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert–Huang transform [J]. Measurement, 2016, 82: 221–239.
[17] 邓四二，王勇，王恒迪. 基于IHT的共振解调技术的滚动轴承故障诊断方法[J]. 航空动力学报, 2012, 27(1):69-74.
Deng Sier, Wang Yong, Wang Hengdi. Resonance demodulation technique based on IHT for rolling bearings fault diagnosis [J]. Journal of Aerospace Power, 2012, 27(1): 69-74.
[18] Yanxue Wang, Richard Markert, Jiawei Xiang, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system [J]. Mechanical Systems and Signal Processing, 2015, 60-61: 243–251.