基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断

任学平,李攀,王朝阁,张超

振动与冲击 ›› 2018, Vol. 37 ›› Issue (15) : 6-13.

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (15) : 6-13.
论文

基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断

  • 任学平,李攀,王朝阁,张超
作者信息 +

Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator

  • REN Xueping,LI Pan,WANG Chaoge,ZHANG Chao
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文章历史 +

摘要

针对滚动轴承早期故障比较微弱,特征信息难以提取且变分模态分解(Variational Mode Decompositong,VMD)中分解层数k的大小需要使用者反复尝试而不能有效确定的问题,提出了改进的VMD方法,以能量差作为评价参数自适应地确定分解层数k。在此基础上,将改进的VMD与包络导数能量算子结合,提出了VMD与包络导数能量算子的轴承早期故障诊断方法。首先采用VMD对轴承故障振动信号进行分解,根据能量差曲线确定最佳的分解层数k;然后依据峭度准则,从分解得到的k个本征模态分量中选取敏感分量进行重构;最后用包络导数能量算子对重构信号进行解调分析,从其能量谱中便可准确地提取轴承的故障特征信息。通过仿真信号和实验数据的分析,验证了该方法的有效性与可行性。

Abstract

Aiming at rolling bearings’ early fault features being weaker and difficult to extract, and the decomposition layer number k in VMD being too difficult to determine, the improved VMD method was proposed. Energy difference was taken as an evaluation parameter to adaptively determine the decomposing layer number k. Then, the improved VMD was combined with the envelope derivative operator, a rolling bearing early fault feature diagnosis method was proposed. Firstly, a rolling bearing’s original fault vibration signal was decomposed with the VMD. According to the energy difference curve, the optimal value of k was determined. Secondly, according to the kurtosis criterion, sensitive components were selected from k IMFs obtained with decomposition to reconstruct a signal. The reconstructed signal was demodulated and analyzed with the envelope derivative operator. The rolling bearing’s fault feature information was extracted correctly from the energy spectrum of the reconstructed signal. Through analyzing simulated signals and test data, the validity and feasibility of the proposed method was verified.

关键词

变分模态分解 / 包络导数能量算子 / 滚动轴承 / 早期故障

Key words

variational mode decomposing (VMD) / envelope derivative operator / rolling bearing / early fault

引用本文

导出引用
任学平,李攀,王朝阁,张超. 基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击, 2018, 37(15): 6-13
REN Xueping,LI Pan,WANG Chaoge,ZHANG Chao. Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator[J]. Journal of Vibration and Shock, 2018, 37(15): 6-13

参考文献

[1] 董文智, 张超. 基于EEMD分解和奇异值差分谱理论的轴承故障诊断研究[J].机械强度, 2012,34(2): 539-545.
DONG Wen-zhi, ZHANG Chao, Bearing fault diagnosis  method based on EEMD and difference spectrum theory of  singular value[J]. Journal of Mechanical Strength, 2012,34(2):  539-545.
[2] 任学平, 王朝阁, 张玉皓. 基于MCKD-EEMD的滚动轴承微故障特征提取[J]. 机械设计与制造, 2016(8): 193-196.
REN Xue-ping, WANG Chao-ge, ZHANG Yu-hao. Feature  extraction of rolling bearing’s weak fault based on  MCKD-EEMD[J]. Machinery Design & Manufacture,  2016(8): 193-196.
[3] 任学平, 张玉皓, 邢义通,等. 基于角域级联最大相关峭度反褶积的滚动轴承早期故障诊断[J]. 仪器仪表学报, 2015, 36(9):2104-2111.
REN Xue-ping, ZHANG Yu-hao, XING Yi-tong, et al.  Rolling bearing early fault diagnosis based on angular  domain cascade maximum correlation kurtosis  deconvolution[J]. Chinese Journal of Scientific Instrument,  2015, 36(9): 2104-2111.
[4] 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(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(2):38-44.
[5] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[6] 张东, 冯志鹏. 基于变分模式分解和微积分增强能量算子的滚动轴承故障诊断[J]. 工程科学学报, 2016, 38(9): 1327-1334.
ZHNAG Dong, FENG Zhi-peng. Fault diagnosis of rolling  bearings based on variational mode decomposition and  calculus enhanced energy operator[J]. Chinese Journal of  Engineering, 2016, 38(9):1327-1334.
[7] 马增强, 李亚超, 刘政,等. 基于变分模态分解和 Teager 能量算子的滚动轴承故障特征提取[J]. 振动与冲击, 2016, 35(13):134-139.
MA Zeng-qiang, LI Ya-chao, LIU Zheng, et al. Rolling  bearing’s fault feature extraction based on variational mode  decomposition and Teager energy operator[J]. Journal of  Vibration and Shock, 2016, 35(13):134-139
[8] 唐贵基, 王晓龙. 参数优化变分模态分解方法在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 Gui-ji, WANG Xiao-long. Parameter optimized  variational mode decomposition method with application  to incipient fault diagnosis of rolling bearing[J]. Journal of  xi’an jiao tong University. 2015, 49(5): 73-81.
[9] Zeng M, Yang Y, Zheng J, et al. Normalized complex Teager energy operator demodulation method and its application to fault diagnosis in a rubbing rotor system[J]. Mechanical Systems & Signal Processing, 2015, s50–51: 380-399.
[10] Potamianos A, Maragos P. A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation[J]. Signal Processing, 1994, 37(1): 95-120.
[11] 王天金, 冯志鹏, 郝如江,等. 基于Teager能量算子的滚动轴承故障诊断研究[J]. 振动与冲击, 2012, 31(2):1-5.
WANG Tian-jin, FENG Zhi-peng, HAO Ru-jiang, et al. Fault  diagnosis of rolling element bearing based on Teager energy  operater[J]. Journal of Vibration and Shock, 2012, 31(2):1-5.
[12] Cheng J, Yu D, Yu Y. The application of energy operator demodulation approach based on EMD in machinery fault diagnosis[J]. Mechanical Systems & Signal Processing, 2007, 21(2): 668-677.
[13] O'Toole J M, Temko A, Stevenson N. Assessing instantaneous energy in the EEG: a non-negative, frequency-weighted energy operator[C]. Engineering in Medicine and Biology Society. IEEE, 2014:3288-3291.
[14] Imaouchen Y, Kedadouche M, Alkama R, et al. A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection[J]. Mechanical Systems & Signal Processing, 2016, 82:103-116.
[15] 刘长良,武英杰,甄成钢.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报, 2015 , 35(0):1-7.
LIU Chang-liang, WU Ying-jie, ZHEN Cheng-gang. Rolling  bearing fault diagnosis based on variational mode  decomposition and fuzzy C means clustering[J]. Proceedings  of the CSEE, 2015 , 35(0):1-7.
[16] 唐贵基, 王晓龙. 自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J]. 中国电机工程学报, 2015, 35(6):1436-1444.
TANG Gui-ji, WANG Xiao-long. Adaptive maximum  correlated kurtosis deconvolution method and its  application on incipient fault diagnosis of bearing[J].  Proceedings of the CSEE, 2015, 35(6):1436-1444.

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