Yan, Shen, Zhong, Xiang, Shao, Haidong, Ming, Yuhang, Liu, Chao and Liu, Bin (2023). Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization. Reliability Engineering and System Safety, 239 ,
Abstract
The current data-level and algorithm-level based imbalanced fault diagnosis methods have respective limitations such as uneven data generation quality and excessive reliance on minority class information. In response to these limitations, this study proposes a novel digital twin-assisted framework for imbalanced fault diagnosis. The framework begins by analyzing the nonlinear kinetic characteristics of the gearbox and establishing a dynamic simulation model assisted by digital twin technology to generate high-fidelity simulated fault data. Subsequently, a subdomain adaptive mechanism is employed to align the conditional distribution of the subdomains by minimizing the dissimilarity of fine-grained features between the simulated and real-world fault data. To improve the fault tolerance of the model's diagnosis, margin-aware regularization is designed by applying significant regularization penalties to the fault data margins. Experimental results from two gearboxes demonstrate that, compared to the recent data-level and algorithm-level based imbalanced fault diagnosis methods, the proposed framework holds distinct advantages under the influence of highly imbalanced data, offering a fresh perspective for addressing this challenging scenario. In addition, the effectiveness of subdomain adaptive mechanism and margin-aware regularization is verified through the ablation experiment.
Publication DOI: | https://doi.org/10.1016/j.ress.2023.109522 |
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Divisions: | College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing |
Additional Information: | Copyright © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ Funding Information: This research is supported by the National Natural Science Foundation of China (No. 52275104 ) and the Natural Science Fund for Excellent Young Scholars of Hunan Province (No. 2021JJ20017 ). Publisher Copyright: © 2023 Elsevier Ltd |
Uncontrolled Keywords: | Digital twin,Gearbox,Imbalanced fault diagnosis,Margin-aware regularization,Subdomain adaptive mechanism,Safety, Risk, Reliability and Quality,Industrial and Manufacturing Engineering |
Publication ISSN: | 0951-8320 |
Last Modified: | 18 Nov 2024 08:44 |
Date Deposited: | 26 Jul 2023 14:17 |
Full Text Link: | |
Related URLs: |
https://www.sci ... 4362?via%3Dihub
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PURE Output Type: | Article |
Published Date: | 2023-11 |
Published Online Date: | 2023-07-23 |
Accepted Date: | 2023-07-22 |
Authors: |
Yan, Shen
Zhong, Xiang Shao, Haidong Ming, Yuhang Liu, Chao ( 0000-0001-7261-3832) Liu, Bin |
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