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Automated broad transfer learning for cross-domain fault diagnosis

Published:2023-02 Issue: Volume:66 Page:27-41
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

Liu Guokai^ORCID,Shen Weiming^ORCID,Gao Liang^ORCID,Kusiak Andrew

Funder

Fundamental Research Funds for the Central Universities

University of Iowa

China Scholarship Council

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference54 articles.

1. Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics;Jimenez;J Manuf Syst,2020

2. Smart manufacturing must embrace big data;Kusiak;Nature,2017

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

4. Knowledge transfer in fault diagnosis of rotary machines;Liu;IET Collab Intell Manuf,2022

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

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1. Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery;Engineering Applications of Artificial Intelligence;2024-08

2. A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis;Expert Systems with Applications;2024-07

3. Feature structured domain adaptation for quality prediction of cross working conditions in industrial processes;Journal of Manufacturing Systems;2024-06

4. A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery;Advanced Engineering Informatics;2024-04

5. A class-imbalance-aware domain adaptation framework for fault diagnosis of wind turbine drivetrains under different environmental conditions;Ocean Engineering;2024-03

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