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Keywords = BDVMD

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27 pages, 7125 KiB  
Article
Variable-Speed Bearing Fault Diagnosis Based on BDVMD, FRTSMFrBSIE, and Parameter-Optimized GRU-MHSA
by Jie Ma, Jun Wei, Qiao Li and Lei Xia
Processes 2025, 13(2), 498; https://doi.org/10.3390/pr13020498 - 11 Feb 2025
Cited by 2 | Viewed by 931
Abstract
To address the challenges of feature extraction and low classification accuracy in fault diagnosis of variable-speed rolling bearings, this paper proposes an intelligent fault diagnosis method based on bandwidth division variational mode decomposition (BDVMD), fractional domain time-shift multiscale fractional Boltzmann-Shannon interaction entropy (FRTSMFrBSIE), [...] Read more.
To address the challenges of feature extraction and low classification accuracy in fault diagnosis of variable-speed rolling bearings, this paper proposes an intelligent fault diagnosis method based on bandwidth division variational mode decomposition (BDVMD), fractional domain time-shift multiscale fractional Boltzmann-Shannon interaction entropy (FRTSMFrBSIE), and parameter-optimized gated recurrent unit with multi-head self-attention (GRU-MHSA). First, the BDVMD is introduced to decompose and reconstruct signals, obtaining high-quality reconstructed fault signals. Next, the FRTSMFrBSIE is proposed to calculate the entropy of the reconstructed signals and generate a fault feature dataset. Subsequently, the improved dung beetle optimization (IDBO) algorithm is applied to optimize the parameters of the GRU-MHSA model, adaptively determining its optimal configuration. Finally, the fault feature dataset is input into the optimized model for fault classification, achieving a classification accuracy of 98.75%. Experiments conducted on the Ottawa bearing dataset validate the proposed method, and the results demonstrate its effectiveness and superiority in feature extraction and fault classification. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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