2D Characterization Based on MSGMD And Its Application in Gearbox Fault Diagnosis

Abstract

In recent years, the deep learning-based fault diagnosis method has made remarkable achievements, but it is still challenging in the small sample problem. The image texture features of the vibration signal can effectively represent different gearbox states, which is expected to alleviate the dependence on the number of training samples. Therefore, a new time-frequency diagram characterization method based on multi-symplectic geometric modal decomposition (MSGMD) is proposed. Based on the characterization analysis of multi-component simulation signals, it is proved that the MSGMD time-frequency diagram is feasible to characterize signals, and its advantages over other signal decomposition methods. On this basis, a gearbox fault diagnosis method based on MSGMD and convolutional neural network (CNN) is proposed and applied to solve the small sample problem. The experiment results show that the method can achieve more than 95% recognition accuracy even in dealing with small samples (the average number of training samples for each gearbox state is only 22). Compared with other intelligent diagnosis methods, it gets higher recognition accuracy. The above analysis shows that the proposed method is expected to be used in practical engineering gearbox fault diagnosis.