Self-Supervised Defect Representation Learning for Label-Limited Rail Surface Defect Detection
Author:
Affiliation:
1. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Funder
State Key Laboratory of Traction Power, Southwest Jiaotong University
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10336249/10288462.pdf?arnumber=10288462
Reference49 articles.
1. Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings
2. Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising
3. Unsupervised Visual Representation Learning by Context Prediction
4. Bearing Fault Diagnosis Under Multisensor Fusion Based on Modal Analysis and Graph Attention Network
5. A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains
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