Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise-Reference-Cited by-同舟云学术

Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise

Published:2023-03 Issue: Volume:186 Page:109848
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Yu Xiaoxia^ORCID,Tang Baoping^ORCID,Deng Lei

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference36 articles.

1. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings;Liu;Meas. J. Int. Meas. Confed.,2020

2. Artificial intelligence for fault diagnosis of rotating machinery: a review;Liu;Mech. Syst. Signal Process.,2018

3. Rotating machine systems fault diagnosis using semisupervised conditional random field-based graph attention network;Tang;IEEE Trans. Instrum. Meas.,2021

4. Gearbox condition monitoring in wind turbines: a review;Salameh;Mech. Syst. Signal Process.,2018

5. Fault diagnosis for rotary machinery with selective ensemble neural networks;Wang;Mech. Syst. Signal Process.,2018

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