Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis-Reference-Cited by-同舟云学术

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Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis

Published:2024-10 Issue: Volume:250 Page:110252
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Zhang Guowei,Kong Xianguang,Wang Qibin,Du Jingli^ORCID,Wang Jinrui,Ma Hongbo

Publisher

Elsevier BV

Reference48 articles.

1. Cross-domain augmentation diagnosis: an adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions;Li;Reliab Eng Syst Saf,2023

2. Towards trustworthy machine fault diagnosis: a probabilistic Bayesian deep learning framework;Zhou;Reliab Eng Syst Saf,2022

3. Adaptive multispace adjustable sparse filtering: a sparse feature learning method for intelligent fault diagnosis of rotating machinery;Zhang;Eng Appl Artif Intell,2023

4. Multi-source partial domain adaptation method based on pseudo-balanced target domain for fault diagnosis;Zhang;Knowl Based Syst,2023

5. Dual weighted-class adversarial network for rotary machine fault diagnosis using multisource domain with class-inconsistent data;Yang;IEEE/ASME Trans Mechatron,2024

Cited by 1 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. Causal intervention graph neural network for fault diagnosis of complex industrial processes;Reliability Engineering & System Safety;2024-11

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