基于改进自适应变分模态分解的滚动轴承微弱故障诊断  

Early fault diagnosis of rolling bearings based on adaptive variationalmode decomposition and the Teager energy operator

作　　者：谷然 陈捷[1] 洪荣晶[1] 潘裕斌[1] 李媛媛 GU Ran;CHEN Jie;HONG Rongjing;PAN Yubin;LI Yuanyuan(School of Mechanical and Power Engineering,Nanjing Tech University,Nanjing 211816,China;Minth Group,Ningbo 315000,China)

机构地区：[1]南京工业大学机械与动力工程学院,南京211816 [2]敏实集团,浙江宁波315000

出　　处：《振动与冲击》2020年第8期1-7,22,共8页Journal of Vibration and Shock

基　　金：国家自然科学基金(51875273)。

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。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 

分 类 号：TH165.3[机械工程—机械制造及自动化]