A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis-Reference-Cited by-同舟云学术

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A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis

Published:2024-03 Issue: Volume:243 Page:109892
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Zheng Xiaorong^ORCID,Nie Jiahao,He Zhiwei,Li Ping,Dong Zhekang,Gao Mingyu

Funder

National Key Research and Development Program of China

Key Research and Development Program of Zhejiang Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference37 articles.

1. Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method;Chen;Reliab Eng Syst Saf,2023

2. Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy;Li;Reliab Eng Syst Saf,2023

3. Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy;Zhao;Reliab Eng Syst Saf,2023

4. Deep discriminative transfer learning network for cross-machine fault diagnosis;Qian;Mech Syst Signal Process,2023

5. Fault diagnosis for small samples based on attention mechanism;Zhang;Measurement,2022

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Semi-supervised source-free domain adaptation method via diffusive label propagation for rotating machinery fault diagnosis;Reliability Engineering & System Safety;2024-12

2. DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows;Reliability Engineering & System Safety;2024-10

3. Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation;Reliability Engineering & System Safety;2024-10

4. Sparse measure of bearing fault features based on Legendre wavelet multi-scale multi-mode Entropy;Computers and Electrical Engineering;2024-05

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