Adaptive k-Sparsity-Based Weighted Lasso for Bearing Fault Detection
Author:
Affiliation:
1. School of Mechanical Engineering, Tongji University, Shanghai, China
Funder
National Natural Science Foundation of China
Action Plan for Scientific and Technological Innovation of Shanghai Science and Technology Commission
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9722574/09681883.pdf?arnumber=9681883
Reference35 articles.
1. Average combination difference morphological filters for fault feature extraction of bearing
2. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform
3. Signal Recovery by Proximal Forward-Backward Splitting
4. Least angle regression
5. A Note on Simultaneous Isolation of Sensor and Actuator Faults
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