Open Access

ARTICLE

Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

College of Mechanical and Vehicle Engineering, Changchun University, Changchun, 130012, China

* Corresponding Author: Yingyong Zou. Email:

(This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)

Computers, Materials & Continua 2025, 83(3), 4699-4723. https://doi.org/10.32604/cmc.2025.062625

Received 23 December 2024; Accepted 19 February 2025; Issue published 19 May 2025

Abstract

High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is used to obtain global time series features of the signal. Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal. Meanwhile, to improve the accuracy, Variable Modal Decomposition (VMD) is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis, which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information. The feasibility of the model is tested and experimentally validated using publicly available datasets. The experimental results show that the accuracy of the model proposed in this paper is significantly improved compared to the traditional WDCNN, BILSTM, and WDCNN-BILSTM models.

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