دورية أكاديمية
Diffusion‐UDA: Diffusion‐based unsupervised domain adaptation for submersible fault diagnosis
| العنوان: | Diffusion‐UDA: Diffusion‐based unsupervised domain adaptation for submersible fault diagnosis |
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| المؤلفون: | Penghui Zhao, Xindi Wang, Yi Zhang, Yang Li, Hongjun Wang, Yang Yang |
| المصدر: | Electronics Letters, Vol 60, Iss 3, Pp n/a-n/a (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: | artificial intelligence, fault diagnosis, signal processing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: | Abstract Deep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its structure and operational environment. Consequently, this diversity gives rise to variations in the types of sensor signals and their associated data distributions that require analysis. Unsupervised domain adaptation (UDA) uses transferable knowledge derived from the source domain and applies it to an unlabelled target domain in order to improve the reusability of pre‐existing models and data. Inspired by the diffusion models that have the robust capabilities to transform data distributions across a large gap, we propose a novel diffusion‐based unsupervised domain adaptation (diffusion‐UDA) model, which leverages contrastive learning to alleviate the challenges of cross‐domain analysis for fault diagnosis within different systems of the deep‐sea submersible. Experimental results show the proposed method achieves state‐of‐the‐art performance on various benchmarks. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1350-911X 0013-5194 |
| Relation: | https://doaj.org/toc/0013-5194; https://doaj.org/toc/1350-911X |
| DOI: | 10.1049/ell2.13122 |
| URL الوصول: | https://doaj.org/article/06d9781e483044988bad37d7cd382262 |
| رقم الأكسشن: | edsdoj.06d9781e483044988bad37d7cd382262 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1350911X 00135194 |
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| DOI: | 10.1049/ell2.13122 |