Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis-Reference-Cited by-同舟云学术

Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis

Published:2024-03 Issue: Volume:288 Page:111499
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

He Chao^ORCID,Shi Hongmei^ORCID,Liu Xiaorong,Li Jianbo

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference56 articles.

1. Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study;Zhao;IEEE Trans. Instrum. Meas.,2021

2. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges;Li;Mech. Syst. Sig. Process.,2022

3. Fault diagnosis in rotating machines based on transfer learning: literature review;Misbah;Knowl.-Based Syst.,2024

4. Transfer learning based on improved stacked autoencoder for bearing fault diagnosis;Luo;Knowl.-Based Syst.,2022

5. A novel bearing fault diagnosis method based on few-shot transfer learning across different datasets;Zhang;Entropy,2022

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