Fault diagnosis of rolling bearing based on optimized Tunable-Q Wavelet Transform
Author:
Affiliation:
1. Xinjiang University,School of Electrical Engineering,Urumqi,China
2. CSIC of HaiWei (Xinjiang) New Energy Co., Ltd.,Urumqi,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295807.pdf?arnumber=10295807
Reference10 articles.
1. Fault diagnosis method based on med-vmd and optimized SVM for rolling bearings[J];yao;China Mechanical Engineering,2017
2. Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks
3. The diagnosis approach for rolling bearing fault based on Kurtosis criterion EMD and Hilbert envelope spectrum
4. Fault feature extraction of a rotor system based on local mean decomposition and Teager energy kurtosis
5. Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine
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