Open Access

ARTICLE

Multi-Scale Fusion Network Using Time-Division Fourier Transform for Rolling Bearing Fault Diagnosis

Ronghua Wang¹, Shibao Sun^1,*, Pengcheng Zhao^1,*, Xianglan Yang², Xingjia Wei¹, Changyang Hu¹

1 School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China
2 School of Computer Science, Luoyang Institute of Science and Technology, Luoyang, 471000, China

* Corresponding Authors: Shibao Sun. Email: ; Pengcheng Zhao. Email:

Computers, Materials & Continua 2025, 84(2), 3519-3539. https://doi.org/10.32604/cmc.2025.066212

Received 01 April 2025; Accepted 21 May 2025; Issue published 03 July 2025

Abstract

The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to extract frequency-domain features in a segmented manner. A cross-attention mechanism is introduced to facilitate the fusion of the extracted time-frequency domain features. The performance of the proposed method is validated using the CWRU and Ottawa datasets. The results show that the average accuracy of MSFN under complex noisy signals is 97.75% and 94.41%. The average accuracy under variable load conditions is 98.68%. This demonstrates its significant application potential compared to existing methods.

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Keywords

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