Source-free cross-domain fault diagnosis of rotating machinery using the Siamese framework-Reference-Cited by-同舟云学术

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Source-free cross-domain fault diagnosis of rotating machinery using the Siamese framework

Published:2024-09 Issue: Volume:300 Page:112179
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Ma Chenyu,Tu Xiaotong^ORCID,Zhou Guanxing,Huang Yue,Ding Xinghao

Publisher

Elsevier BV

Reference33 articles.

1. A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems;Qiao;IEEE Trans. Ind. Electron.,2015

2. Applications of machine learning to machine fault diagnosis: A review and roadmap;Lei;Mech. Syst. Signal Process.,2020

3. Novel joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis;Hu;Knowl.-Based Syst.,2023

4. Degradation modeling and remaining useful life prediction for dependent competing failure processes;Yan;Reliab. Eng. Syst. Saf.,2021

5. Adaptive knowledge transfer by continual weighted updating of filter kernels for few-shot fault diagnosis of machines;Xing;IEEE Trans. Ind. Electron.,2021

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1. Remaining useful life prediction across operating conditions based on deep subdomain adaptation network considering the weighted multi-source domain;Knowledge-Based Systems;2024-10

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