A fault diagnosis method of bearings based on deep transfer learning-Reference-Cited by-同舟云学术

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A fault diagnosis method of bearings based on deep transfer learning

Published:2023-01 Issue: Volume:122 Page:102659
ISSN:1569-190X
Container-title:Simulation Modelling Practice and Theory
language:en
Short-container-title:Simulation Modelling Practice and Theory

Author:

Huang Min,Yin Jinghan,Yan Shumin,Xue Pengcheng

Funder

Natural Science Foundation of Guangdong Province

Publisher

Elsevier BV

Subject

Hardware and Architecture,Modeling and Simulation,Software

Reference40 articles.

1. Bearing Fault Model for Induction Motor With Externally Induced Vibration;Immovilli;IEEE Trans. Ind. Electron.,2013

2. Fault diagnosis of motor bearings based on a one-dimensional fusion neural network;Jian;Sensors,2019

3. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings;Gan;Mech. Syst. Sig. Process.,2016

4. Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features;Ahmed;Mech. Syst. Sig. Process.,2018

5. Hybrid adversarial network for unsupervised domain adaptation;Zhang;Inf. Sci.,2020

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1. A Novel Method for Bearing Fault Diagnosis Based on Novel Feature Sets With Machine Learning Technique;Journal of Tribology;2024-09-13

2. Deep semi-supervised transfer learning method on few source data with sensitivity-aware decision boundary adaptation for intelligent fault diagnosis;Expert Systems with Applications;2024-09

3. Bearing fault diagnosis method based on multi-domain feature fusion and heterogeneous network under small sample conditions;Signal, Image and Video Processing;2024-08-08

4. Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information;Reliability Engineering & System Safety;2024-08

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

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