Improved Shift-Invariant Sparse Parsing of Mechanical Fault Based on Feature Atom
Author:
Affiliation:
1. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
2. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, China
Funder
National Natural Science Foundation of China
Postdoctoral Fellowship Program of CPSF
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/19/10367905/10616185.pdf?arnumber=10616185
Reference47 articles.
1. Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation
2. An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions
3. Bearing Fault Diagnosis With Frequency Sparsity Learning
4. A recursive sparse representation strategy for bearing fault diagnosis
5. A Novel Noise-Aided Fault Feature Extraction Using Stochastic Resonance in a Nonlinear System and Its Application
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