Abstract
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.
Highlights
Rolling element bearings are the most critical parts in the rotating machinery
The flow of rolling element bearing fault diagnosis method based on improved variational mode decomposition (IVMD) and deep convolutional neural network (DCNN) is shown in Fig. 6, and specific steps are shown as follows: Steps 1: Process the original vibration data of rolling element bearings by sliding window, and the vibration data samples in different states are obtained
In this paper, aiming at the non-stationarity characteristic of the vibration signal of rolling bearings, a bearing fault diagnosis method based on IVMD and DCNN is proposed
Summary
Rolling element bearings are the most critical parts in the rotating machinery. So accurate and reliable fault diagnosis of rolling element bearing is of great significance to maintain normal operation of mechanical equipment [1, 2]. Li [12] proposed a parametric adaptive VMD method to reduce the influence of artificially setting decomposition number, and applied it to the fault diagnosis of planetary gear box To solve this problem, this paper proposes an IVMD method to select the appropriate mode decomposition number according to the change of instantaneous frequency mean of modal component. Jia [17] proposed a deep normalized convolutional neural network to solve the problem of imbalanced fault classification of machinery and applied it to bearing fault diagnosis These studies show that CNN can reduce the uncertainty caused by artificial feature design and extraction in fault diagnosis and the dependence on expert diagnostic knowledge.
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