Improved Knowledge Transfer based on Stacked Denoising Autoencoder and Graph Convolutional Network for Fault Diagnosis of Rolling Bearings
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731
Funder
Natural Science Foundation of Sichuan Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10482420/10482375/10482769.pdf?arnumber=10482769
Reference23 articles.
1. Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis
2. Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review
3. Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery
4. Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery
5. A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery
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