A Mask Self-Supervised Learning-Based Transformer for Bearing Fault Diagnosis With Limited Labeled Samples
Author:
Affiliation:
1. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China
2. China Petroleum and Chemical Corporation, Guangzhou Branch, Guangzhou, China
Funder
Innovative Team Project of the Ordinary University of Guangdong Province
Guangdong Special Project in the Key Field of Artificial Intelligence for the Ordinary University
Guangzhou Key Laboratory Construction Project
Guangzhou Key Research and Development Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10124342/10098752.pdf?arnumber=10098752
Reference37 articles.
1. A Time Series Transformer based method for the rotating machinery fault diagnosis
2. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
3. A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings
4. A fault diagnosis method for rolling element bearings based on ICEEMDAN and Bayesian network
5. Research on bearing fault diagnosis method based on transformer neural network
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2. An Unsupervised Framework Based on Dual-Domain Contrastive Learning and Tri-Indicator Joint Alarm Strategy for Early Fault Detection of Bearing;IEEE Sensors Journal;2024-08-15
3. Fast-Activated Minimal Gated Unit: Lightweight Processing and Feature Recognition for Multiple Mechanical Impact Signals;Sensors;2024-08-14
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