Multivariate complex modulation model decomposition and its application to gear fault diagnosis

Abstract

The single-channel vibration signal of gears contains limited information, and it is easily affected by the transmission path. Therefore, multi-channel signals should be used for fault diagnosis because multi-channel gear signals usually contain richer and more comprehensive information about the equipment status. But the existing single-channel signal processing methods are not applicable to multi-channel signals, and each of the existing multi-channel signal processing methods has its own limitations. Therefore, this study proposes a new multi-channel signal processing method called multivariate complex modulation model decomposition (MCMMD), by which multi-channel signals can be decomposed accurately and adaptively at the same time. The core of the method is to iteratively update the model parameters by combining all channel signals to acquire the pattern alignment property. The decomposition performance is analyzed first. Then MCMMD is applied to simulation and experimental multi-channel gear fault signals. For comparison, ensemble empirical mode decomposition (EEMD), multivariate empirical mode decomposition (MEMD), multivariate variational mode decomposition (MVMD), multivariate local characteristic-scale decomposition (MLCD), and completely adaptive projection MLCD (CAPMLCD) are presented as well. The results show that the decomposition accuracy and robustness of MCMMD are better than those of the other methods compared. Therefore, MCMMD is an accurate and effective multi-channel signal processing method.