Enhanced Rolling Bearing Fault Diagnosis Combining Novel Fluctuation Entropy Guided-VMD with Neighborhood Statistical Model

Yuan, Xiong; Liu, Hui; Zhang, Huijie

doi:10.3390/app13010192

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Bearings, being crucial components of these machines, play a particularly significant role in their monitoring and diagnosis [1]. Research findings indicate that rolling bearing failures account for 30% of all rotating machinery failures [2][3][4]. Therefore, fault diagnosis of rolling bearings holds immense importance.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Morphological Network Fault Diagnosis Method for Rolling Bearings Based on Acoustic Array Signal

Luo,

Yang,

Kang

et al. 2023

Applied Sciences

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To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, a mathematical morphological network layer is constructed by leveraging the advantages of a multi-scale enhanced top-hat morphological operator (MEAVGH) that can extract positive and negative pulses, which are then integrated into the deep learning network. Second, the input signal undergoes processing with different scale structural elements (SEs) to obtain multi-branch data. This is followed by channel attention and spatial attention mechanism-based weighting of the generated multi-branch data. Finally, the fused information is fed to the neural network to yield the final result. The experimental results demonstrate the efficacy of the proposed method in extracting fault feature information, achieving a fault classification accuracy of 98.56%. Furthermore, the algorithm exhibits robustness and high training efficiency. Comparative analysis reveals that the proposed method outperforms other approaches regarding cluster analysis, accuracy, recall rate, and computational efficiency. These findings further highlight the advantages of MMNet in acoustic signal-based fault diagnosis for rolling bearings.

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