Rolling Bearing Fault Diagnosis Method Based on MCMF and SAIMFE

Abstract

Fault diagnosis of rolling bearing is important for ensuring the safe operation of industrial machinery. In order to improve diagnosis accuracy of bearing fault, a rolling bearing fault diagnosis method based on multiscale combined morphological filter (MCMF) and self-adaption improved multiscale fuzzy entropy (SAIMFE) is proposed in this paper. First, the MCMF is designed to eliminate noise and preserve fault information more effectively. Second, SAIMFE is proposed to extract bearing fault features, and the optimized scale factor of SAIMFE is determined based on the absolute skewness. Third, some experiments are completed to demonstrate the effectiveness and superiority of the proposed method. The experimental results show that the proposed method not only has high diagnosis accuracy but also less dependent on the diagnosis model.

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

“…16 Liu et al 17 proposed a new algorithm for bearing fault diagnosis by combining multi-scale one-dimensional convolutional neural network (1-D CNN) with sparse wavelet decomposition feature extraction. 17 Meng et al 18 proposed a rolling bearing fault diagnosis method based on multiscale combined morphological filter (MCMF) and self-adaptive improved multiscale fuzzy entropy (SAIMFE), and the experimental results show that the method not only has high diagnostic accuracy but also has less dependence on the diagnostic model. 18 For the rolling bearing vibration signal is a typical non-smooth signal, Sun et al 19 proposed a new fault diagnosis method based on the improved Manhattan distance in Symmetrized Dot Pattern (SDP) image.…”

“…17 Meng et al 18 proposed a rolling bearing fault diagnosis method based on multiscale combined morphological filter (MCMF) and self-adaptive improved multiscale fuzzy entropy (SAIMFE), and the experimental results show that the method not only has high diagnostic accuracy but also has less dependence on the diagnostic model. 18 For the rolling bearing vibration signal is a typical non-smooth signal, Sun et al 19 proposed a new fault diagnosis method based on the improved Manhattan distance in Symmetrized Dot Pattern (SDP) image. 19 In order to solve the problem that rolling bearings have too many characteristic parameters with remarkable randomness and severe signal coupling during operation, Zhou et al 20 proposed a new bearing fault diagnosis method by combining Gramian angular field (GAF) and dense network.…”