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RESEARCH ARTICLE

Rolling bearing fault diagnosis method based on GJO–VMD, multiscale fuzzy entropy, and GSABO–BP neural network

Jingsong Zhang1† ,  Xiaolong Zhou2† ,  Soo Siang Yang1* ,  Min Keng Tan1 ,  Yanzhen Wang2 ,  Bin Zheng1 ,  Jing Zhe2 ,  Haoyu Li2
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1 Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
2 College of Mechanical Engineering, Beihua University, Jilin, China
†These authors contributed equally to this work.
IJOCTA, 025170086 https://doi.org/10.36922/IJOCTA025170086
Received: 21 April 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published online: 11 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Accurate fault diagnosis of rolling bearings is hindered by the weak nature of early fault signals and the limited availability of labeled data, especially under small-sample conditions. To overcome these challenges, this paper proposes a novel method combining golden jackal optimization (GJO) with improved variational mode decomposition (VMD), enhanced feature extraction, and optimized classification. First, GJO is used to optimally determine the key decomposition parameters of VMD, thereby improving the accuracy of vibration signal decomposition. A comprehensive discrimination factor algorithm then selects fault-sensitive intrinsic mode functions, and the signal is reconstructed to enhance fault characteristics. Multiscale fuzzy entropy is calculated from the reconstructed signals at multiple scales to build distinct state feature vectors. These vectors are fed into a back-propagation neural network optimized via the golden sine subtraction-average-based optimizer for precise fault classification. The method’s effectiveness is verified through simulation and experimental data. Compared with conventional approaches, it shows superior performance in extracting weak fault features and maintaining high diagnostic accuracy under small-sample scenarios. This integrated framework presents a robust solution for rolling bearing fault diagnosis.

Keywords
Rolling bearing
Fault diagnosis
Variational mode decomposition
Golden jackal optimization
Multiscale fuzzy entropy
Golden sine subtraction-average-based optimizer–back-propagation neural network
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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