基于MVMD-MOMEDA的齿轮箱故障诊断方法

Gearbox fault diagnosis method based on MVMD-MOMEDA

针对齿轮箱振动信号受复杂传递路径、强背景噪声的影响导致早期微弱故障难以诊断的问题,提出了一种基于多元变分模态分解(MVMD)和多点最优最小熵反褶积调整(MOMEDA)的齿轮箱故障诊断方法。首先,利用MVMD将融合后的多通道振动信号进行模态分解,得到一系列表征信号局部特征的IMF分量;其次,引入峭度值(Ku),选取最佳模态进行信号重构,剔除含噪声分量高的IMF;最后,对重构信号进行MOMEDA特征提取以识别故障频率,从而进行故障诊断。结果表明,所提故障诊断方法可以有效剔除噪声分量的干扰,识别出信号中的故障冲击成分及其倍频进而确定故障类型。MVMD-MOMEDA方法解决了在单一通道问题上无法处理多源信号的缺点以及早期微弱故障特征难以提取等问题,可为故障诊断和多源信号处理提供参考。

Aiming at the problem that the early weak fault diagnosis of gearbox vibration signal is difficult due to the influence of complex transmission path and strong background noise,a gearbox fault diagnosis method based on multivariate variational mode decomposition(MVMD)and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)was proposed.Firstly,MVMD was used to decompose the multi-channel vibration signals after fusion,and a series of IMF components representing the local characteristics of the signals were obtained.Secondly,the kurtosis value(Ku)was introduced to select the best mode for signal reconstruction,and the IMF with high noise content was eliminated.Finally,the fault frequency was identified by MOMEDA feature extraction to the reconstructed signal to achieve the purpose of fault diagnosis.The results show that the proposed fault diagnosis method can effectively eliminate the interference of the noise components,identify the fault impact components in the signal and its frequency doubling,and then determine the fault type.The MVMD-MOMEDA method solves the problems such as the inability to deal with multi-source signals in a single channel and the difficulty in extracting early weak fault features,which provides reference for fault diagnosis and multi-source signal processing.