An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

Author:

Li Jipu,Zhang XiaogeORCID,Yue Ke,Chen Junbin,Chen ZhuyunORCID,Li WeihuaORCID

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Hong Kong Polytechnic University

National Natural Science Foundation of China

Special Project for Research and Development in Key Areas of Guangdong Province

The Hong Kong Polytechnic University Department of Industrial and Systems Engineering

Publisher

Elsevier BV

Reference49 articles.

1. rgfc-Forest: An enhanced deep forest method towards small-sample fault diagnosis of electromechanical system;Ming;Expert Systems with Applications,2023

2. Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning;Zhang;Expert Systems with Applications,2023

3. Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis;Gao;Expert Systems with Applications,2024

4. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study;Li;Mechanical Systems and Signal Processing,2022

5. A spectral self-focusing fault diagnosis method for automotive transmissions under gear-shifting conditions;Li;Mechanical Systems and Signal Processing,2023

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