Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Maoyou Ye,Minping Jia,Xiaoan Yan

doi:10.3390/e23060762

Abstract

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

Highlights

With the vigorous development of the machinery industry, rolling bearings have become an important component of rotating machinery and are widely used in generators, gas engines and other kinds of rotating machinery [1,2]
Sun et al [10] proposed a fast bearing fault diagnosis method based on ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive bayes (NB)
This paper proposes a bearing fault diagnosis method based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM)

Summary

Introduction

With the vigorous development of the machinery industry, rolling bearings have become an important component of rotating machinery and are widely used in generators, gas engines and other kinds of rotating machinery [1,2]. Common vibration signal decomposition methods include empirical mode decomposition (EMD) [6], wavelet transform (WT) [7], variational mode decomposition (VMD) [8,9], etc. EMD has been widely addressed by many scholars and is applied in the field of bearing fault diagnosis. Sun et al [10] proposed a fast bearing fault diagnosis method based on ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive bayes (NB). EMD has some disadvantages, such as end effect and mode aliasing [11]

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Results

Conclusion