Application of an Oversampling Method Based on GMM and Boundary Optimization in Imbalance-Bearing Fault Diagnosis
Author:
Affiliation:
1. School of Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China
2. Yunnan Vocational College of Mechanical and Electrical Technology, Kunming, China
Funder
National Natural Science Foundation of China
Key Scientific Research Projects of Yunnan Province
Yunnan Provincial School Education Cooperation Key Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10411967/10144658.pdf?arnumber=10144658
Reference30 articles.
1. Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring
2. A Bearing Fault Diagnosis Method Based on Enhanced Singular Value Decomposition
3. Intelligent fault diagnosis of mechanical equipment under varying working condition via iterative matching network augmented with selective Signal reuse strategy
4. Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning
5. Enhanced Discriminate Feature Learning Deep Residual CNN for Multitask Bearing Fault Diagnosis With Information Fusion
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