AVMD-IMOMEDA在滚动轴承声学复合故障诊断的应用

Separation and extraction of composite fault features of rolling bearing acoustic signals based on AVMD-IMOMEDA

针对滚动轴承声信号存在较强的背景噪声干扰,微弱故障特征信息难以有效提取等问题,并考虑到声信号非接触式测量的优势。提出一种参数自适应变分模式分解结合改进多点最优最小熵反褶积(improve multipoint optimal minimum entropy deconvolution adjusted, MOMEDA)的复合故障声学诊断方法;采用综合指标解决变分模态分解(variational mode decomposition, VMD)参数自适应选择问题,利用最大加权峭度识别最优分量并重构信号,增强与故障特征相关的脉冲特征信息;结合IMOMEDA方法从重构信号中分离提取周期性的脉冲信号,通过包络解调获取故障特征频率。仿真信号和试验信号验证了该方法的有效性,与传统VMD、MOMEDA、VMD-MCKD(maximum correlation kurtosis deconvolution)方法进行比较,凸显了方法的优越性。

Aiming at the problems of strong background noise interference of rolling bearing acoustic signals and difficulty in effectively extracting weak fault characteristic information,and considering the advantages of non-contact measurement of acoustic signals,a composite fault acoustic diagnosis method was proposed combining parameter adaptive variational mode decomposition(AVMD)and improved multi-point optimal minimum entropy deconvolution adjusted(IMOMEDA).The comprehensive index(CI)was used to solve the problem of adaptive selection of VMD parameters,and the maximum weighted kurtosis(WK)was used to identify the optimal component and reconstruct the signal,so as to enhance the pulse characteristic information related to fault characteristics.Combined with the IMOMEDA method,the periodic pulse signal was extracted from the reconstruction signal,and the fault characteristic frequency was obtained through envelope demodulation.Simulation signals and experimental signals verify the effectiveness of the proposed method.Compared with the traditional VMD,MOMEDA,VMD-MCKD methods,the superiority of the proposed method is highlighted.