Bearing fault diagnosis based on improved VMD and DCNN

Vibration signal produced by rolling element bearings has obvious non-stationary and nonlinear characteristics, and it's necessary to preprocess the original signals to obtain better diagnostic results. This paper proposes an improved variational mode decomposition (IVMD) and deep convolutional neural network (DCNN) method to realize the intelligent fault diagnosis of rolling element bearings. Firstly, to solve the problem that the number of decomposed modes of variational mode decomposition (VMD) needs to be preset, an IVMD method is proposed, where the mode number can be determined adaptively according to the curve of the instantaneous frequency mean of mode functions. With this method, the vibration signal can be decomposed into a series of modal components containing bearing fault characteristic information. Then, DCNN is employed to fuse these multi-scale modal components, which can automatically learn fault features and establish bearing fault diagnosis model to realize intelligent fault diagnosis eventually. Experimental analysis and comparison results verify that the proposed method can effectively enhance the bearing fault features and improve the diagnosis accuracy.