An Improved Deep Transfer Learning Method for Rotating Machinery Fault Diagnosis Based on Time Frequency Diagram and Pretraining Model
Author:
Affiliation:
1. Institute of Energy, Hefei Comprehensive National Science Center, Hefei, China
2. Division of Control and Computer Application, Institute of Plasma Physics, and the Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
Funder
National Natural Science Foundation of China
National MCF Research and Development Program of China
Institute of Energy, Hefei Comprehensive National Science Center
University Synergy Innovation Program of Anhui Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10379186.pdf?arnumber=10379186
Reference33 articles.
1. A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
2. Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis
3. Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings
4. Multi-scale deep intra-class transfer learning for bearing fault diagnosis
5. Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains
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