# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 1 Introduction
[5] used EMD for the feature extraction of the vibration signal and used a variety of classifiers to classify the fault state of the rolling bearing, which gained a kind effect. However, EMD has defects in the endpoint effect and mode overlap, resulting in inaccurate decomposition results [6]. On this basis, [PERSON] et al. [7] proposed the ensemble empirical mode decomposition (EEMD) method. By introducing uniformly distributed white noise in the decomposition process, the noise of the signal itself is masked by several occasions of artificially added noise. At the same time, the decomposition results are averaged. The more frequent the average processing times are, the lower the impact of the noise on the decomposition, and then, the mode mixing problem of traditional EMD is solved. To improve the computational efficiency and residual noise problem of EEMD, complete ensemble empirical mode decomposition with adaptive noise (CEEMDWAN) adds white noise to the residual value and calculates the mean value of the IMF component every time the first-order IMF component is obtained in the decomposition process and iterates it step by step to achieve better modal decomposition results. On this basis, to further optimize the noise control and decomposition accuracy of cemdan, [PERSON] et al. [8] proposed the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDWAN) method to improve the introduction of a more flexible adaptive noise strategy and reduce false modes and noise residue. LMD analysis is based on the local smoothness of the signal. [PERSON] et al. [9] combined minimum entropy deconvolution (MED) with LMD, used MED as a filter to denoise the signal, and used the LMD method to decompose the processed signal, providing a solution for the extraction of weak characteristic signals with strong interference conditions. Compared with EMD,LMD does not have the problem of mode overlapping in the decomposition process, but it will be affected by the local smoothness of the signal. In the case of poor smoothness of the signal, the decomposition accuracy will decline. VMD overcomes the shortcomings of EMD, such as endpoint effect and pattern overlap, and has high decomposition efficiency. However, in the application of VMD, how to choose the decomposition times and penalty factor is a problem that needs to be solved. [PERSON] et al. (2018) used the whale algorithm to adaptively select parameters. Although appropriate parameter values can be obtained, many iterative calculations are required, and the calculation efficiency is low. [PERSON] et al. (2018) used kurtosis as the evaluation index and selected the reconstructed signal with the largest kurtosis value as the optimal decomposition, but it was affected by the strong impact of some signals, resulting in the instability of decomposition times. The problem of fault feature extraction also needs to be solved. For subsequent fault diagnosis, representative features are required to be extracted from the original signal. From the point of view of signal processing, [PERSON] et al. (2018) analyzed the specific characteristics of rolling element-bearing signals. From the point of view of cyclostationarity, the discrete/random separation (DRS) method was used for signal separation, spectral kurtosis was used to identify the frequency band with the most significant impulsive, and \"envelope analysis\" was used for the final diagnosis to achieve the results. Alternatively, [PERSON] et al. (2018) further used the cepstrum pre-whitening method for signal denoising and used cyclostationary technology, including square envelope spectrum and improved envelope spectrum, to correctly identify the faults of specific bearing components, and achieved good results. From the perspective of deep learning, the features in the signal can be automatically learned by constructing a multi-layer neural network to convert the original signal into a higher-level and more representative feature representation ([PERSON] et al., 2018). [PERSON] et al.