Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching-Reference-Cited by-同舟云学术

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Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching

Published:2023-03 Issue: Volume:231 Page:109036
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Liu Shaowei,Jiang Hongkai,Wu Zhenghong,Yi Zichun,Wang Ruixin

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference38 articles.

1. A multi-layer spiking neural network-based approach to bearing fault diagnosis;Zuo;Reliab Eng Syst Saf,2022

2. Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings;Ding;Reliab Eng Syst Saf,2022

3. Multi-scale deep intra-class transfer learning for bearing fault diagnosis;Wang;Reliab Eng Syst Saf,2020

4. Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises;Yan;Robot CIM-INT Manuf,2023

5. Machine learning for reliability engineering and safety applications: review of current status and future opportunities;Xu;Reliab Eng Syst Saf,2021

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1. A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples;Reliability Engineering & System Safety;2024-12

2. Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis;Expert Systems with Applications;2024-12

3. Adaptive centroid prototype-based domain adaptation for fault diagnosis of rotating machinery without source data;Reliability Engineering & System Safety;2024-11

4. Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy;Reliability Engineering & System Safety;2024-11

5. Revolution and peak discrepancy-based domain alignment method for bearing fault diagnosis under very low-speed conditions;Expert Systems with Applications;2024-10

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