Sparse Bayesian Learning Approach for Compound Bearing Fault Diagnosis
Author:
Affiliation:
1. College of Information Science and Technology, Donghua University, Shanghai, China
2. Department of Automatic Control, Guangdong University of Technology, Guangzhou, China
3. School of Biomedical Engineering, Shenzhen University, Shenzhen, China
Funder
National Natural Science Foundation of China
Jiangsu Graduate Research and Practice Innovation Program
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/ielx7/9424/10411967/10136804.pdf?arnumber=10136804
Reference35 articles.
1. Matching Synchrosqueezing Wavelet Transform and Application to Aeroengine Vibration Monitoring
2. A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems
3. Magnetic Equivalent Modeling of Stator Currents for Localized Fault Detection of Planetary Gearboxes Coupled to Electric Motors
4. SOSO Boosting of the K-SVD Denoising Algorithm for Enhancing Fault-Induced Impulse Responses of Rolling Element Bearings
5. Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis
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