Intelligent Machine Fault Diagnosis Using Convolutional Neural Networks and Transfer Learning
Author:
Affiliation:
1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09770836.pdf?arnumber=9770836
Reference46 articles.
1. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
2. LSTM-GAN-AE: A Promising Approach for Fault Diagnosis in Machine Health Monitoring
3. A spiking neural network-based approach to bearing fault diagnosis
4. CNN-Based Fault Detection for Smart Manufacturing
5. A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network
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