基于改进自适应变分模态分解的滚动轴承微弱故障诊断

谷然1,陈捷1,洪荣晶1,潘裕斌1,李媛媛2

振动与冲击 ›› 2020, Vol. 39 ›› Issue (8) : 1-7.

PDF(2066 KB)
PDF(2066 KB)
振动与冲击 ›› 2020, Vol. 39 ›› Issue (8) : 1-7.
论文

基于改进自适应变分模态分解的滚动轴承微弱故障诊断

  • 谷然1,陈捷1,洪荣晶1,潘裕斌1,李媛媛2
作者信息 +

Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the Teager energy operator

  • GU Ran1,CHEN Jie1,HONG Rongjing1,PAN Yubin1,LI Yuanyuan2
Author information +
文章历史 +

摘要

滚动轴承早期故障信息微弱,且混有大量背景噪声,难以提取其故障特征。提出了一种改进的自适应变分模态分解(AVMD)与Teager能量谱的微弱故障诊断方法。将最小平均包络熵(MMEE)作为目标函数,自动搜寻影响参数最佳值,确保变分模态分解(VMD)实现最优分解,并提出加权峭度指标(WK)用于选择有效模态分量进行信号重构,对重构信号进行Teager能量谱分析,从而识别故障特征频率。对轴承微弱故障振动信号的研究表明,所提方法改进了传统VMD算法分解精度受参数影响较大,导致信号出现过分解或欠分解的问题;与集合经验模态分解和局部均值分解算法相比所提方法具有更强的噪声鲁棒性和故障信息提取能力。

Abstract

It is difficult to extract early fault information of rolling bearings because the signal is mixed with abundant compounded background noise.An adaptive variational mode decomposition (AVMD) with the Teager energy operator method was proposed.Firstly, the minimum mean envelope entropy (MMEE) was used to search the optimal value of parameters.Subsequently, the weighted kurtosis (WK) was adopted to select the effective modal components for signal reconstruction.Finally, the reconstructed signal was analyzed by Teager energy spectrum to identify fault frequency.The analysis of vibration signals of bearings with weak fault shows that the proposed method improves the decomposition accuracy, and has stronger noise robustness and fault identification ability than ensemble empirical mode decomposition and local mean decomposition.

关键词

自适应变分模态分解(AVMD) / 最小平均包络熵(MMEE) / 加权峭度指标(WK) / Teager能量算子(TEO) / 微弱故障诊断

Key words

adaptive variational modal decomposition(AVMD) / minimum mean envelope entropy(MMEE) / weighted kurtosis(WK) / Teager energy operator(TEO) / weak fault diagnosis

引用本文

导出引用
谷然1,陈捷1,洪荣晶1,潘裕斌1,李媛媛2. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(8): 1-7
GU Ran1,CHEN Jie1,HONG Rongjing1,PAN Yubin1,LI Yuanyuan2. Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the Teager energy operator[J]. Journal of Vibration and Shock, 2020, 39(8): 1-7

参考文献

[1]祝小彦, 王永杰.基于MOMEDA与Teager能量算子的滚动轴承故障诊断[J].振动与冲击, 2018, 37(6): 104-110. ZHU Xiaoyan, WANG Yongjie.Fault diagnosis of rolling bearing based on MOMEDA and teager energy operator[J].Journal of Vibration and Shock,2018, 37(6): 104-110. [2]LI Y B, YANG Y T, WANG X Z, et, al.Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine[J].Journal of Sound and Vibration, 2018, 428: 72-86. [3]WU Z H, HUANG N E.Ensemble empirical mode decomposition: a noise-assisted data analysis method[J].Advance in Adaptive Data Analysis, 2009, 1(1): 903-995. [4]JONATHAN S.The local mean decomposition and its application to EEG perception data[J].Journal of the Royal Society Interface, 2005, 2(5): 443-454. [5]齐咏生, 张二宁, 高胜利,等.基于EEMD-KECA的风电机组滚动轴承故障诊断[J].太阳能学报, 2017, 38(7): 1943-1951. QI Yongsheng, ZHANG Erning, GAO Shengli, et al.Fault diagnosis of rolling bearing of wind turbine based on EEMD-KECA[J].Acta Energiae Solaris Sinica, 2017, 38(7): 1943-1951. [6]钟也磐, 陈卫, 杜炜.基于加强谱峭度的航空发动机齿轮毂故障诊断[J].推进技术, 2017, 38(5): 1140-1146. ZHONG Yenie, CHEN Wei, DU Wei, et al.Fault diagnosis of aero-engine gear hub based on enhanced spectral kurtosis[J].Journal of Propulsion Technology, 2017, 38(5): 1140-1146. [7]WANG L, LIU Z W, MIAO Q, et al.Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings[J].Mechanical Systems and Signal Processing, 2018, 106: 24-39. [8]DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. [9]赵岩, 朱均超, 张宝峰,等.基于VMD与Hilbert谱的旋转机械碰摩故障诊断方法[J].振动、测试与诊断, 2018, 38(2): 381-425. ZHAO Yan, ZHU Junchao, ZHANG Baofeng, et al.Rub impact fault diagnosis method of rotating machinery based on VMD and Hilbert spectrum[J].Journal of Vibration, Measurement & Diagnosis, 2018, 38(2): 381-425. [10]赵洪山, 李浪.基于最大相关峭度解卷积和变分模态分解的风电机组轴承故障诊断方法[J].太阳能学报, 2018, 39(2): 350-358. ZHAO Hongshan, LI Lang.Bearing fault diagnosis method for wind turbine based on maximum correlation kurtosis deconvolution and variational mode decomposition[J].Acta Energiae Solaris Sinica, 2018, 39(2): 350-358. [11]NIU M F, HU Y Y, SUN S L, et al.A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting[J].Applied Mathematical Modelling, 2018(57): 163-178. [12]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, 36(22): 22-28. ZHENG Xiaoxia, ZHOU Guowang, REN Haohan, et al.Rolling bearing fault diagnosis based on variational mode decomposition and permutation entropy[J].Journal of Vibration and Shock, 2017, 36(22): 22-28. [13]赵昕海, 张术臣, 李志深,等.基于VMD的故障特征信号提取方法[J].振动、测试与诊断, 2018, 38(1): 12-19. ZHAO Xinhai, ZHANG Shuchen, LI Zhishen, et al.Fault feature extraction method based on VMD[J].Journal of Vibration, Measurement & Diagnosis, 2018, 38(1): 12-19. [14]边杰.基于遗传算法参数优化的变分模态分解结合1.5维谱的轴承故障诊断[J].推进技术, 2017, 38(7): 1618-1624. BIAN Jie.Parameter optimization of genetic algorithm based on variational mode decomposition combined with 1.5 dimensional spectrum for bearing fault diagnosis[J].Journal of Propulsion Technology, 2017, 38(7): 1618-1624. [15]REN G, JIA J D, MEI J M, et al.A feature extraction method based on VMD and RDT and its application in engine crankshaft bearing fault[Z].Preprints, 2018. [16]赵洪山, 郭双伟, 高夺.基于奇异值分解和变分模态分解的轴承故障特征提取[J].振动与冲击, 2016, 35(22): 183-188. ZHAO Hongshan, GUO Shuangwei, GAO Duo.Feature extraction of bearing fault based on singular value decomposition and variational mode decomposition[J].Journal of Vibration and Shock, 2016, 35(22): 183-188. [17]钱林, 康敏, 傅秀清,等.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击, 2017, 36(3): 227-233. QIAN Lin, KANG Min, FU Xiuqing, et al.Application of adaptive morphology based on VMD in bearing fault diagnosis[J].Journal of Vibration and Shock, 2017, 36(3): 227-233.

PDF(2066 KB)

848

Accesses

0

Citation

Detail

段落导航
相关文章

/