Semisupervised Fault Diagnosis for Gearboxes: A Novel Method Based on a Hybrid Classification Network and Weighted Pseudo-Labeling
Author:
Affiliation:
1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
2. School of Mechanical Engineering, Ningxia University, Yinchuan, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Basic Research Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10183770/10144551.pdf?arnumber=10144551
Reference38 articles.
1. Hybrid Pre-Training Strategy for Deep Denoising Neural Networks and Its Application in Machine Fault Diagnosis
2. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
3. A roller bearing fault diagnosis method based on EMD energy entropy and ANN
4. Single and simultaneous fault diagnosis of gearbox via a semi-supervised and high-accuracy adversarial learning framework
5. A Novel Explainable Deep Belief Network Framework and Its Application for Feature Importance Analysis
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1. A novel semi-supervised learning rolling bearing fault diagnosis method based on SNNGAN;Measurement Science and Technology;2024-05-28
2. Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults;Applied Intelligence;2024-03
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