Diagnosis of bearing defects using tunable Q-wavelet transform
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
http://link.springer.com/article/10.1007/s12206-018-0102-8/fulltext.html
Reference24 articles.
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3. Y. Yang, D. Yu and J. Cheng, A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM, Meas. J. Int. Meas. Confed., 40 (2007) 943–950.
4. P. K. Kankar, S. C. Sharma and S. P. Harsha, Fault diagnosis of ball bearings using machine learning methods, Expert Syst. Appl., 38 (2011) 1876–1886.
5. S. Kavathekar, N. Upadhyay and P. K. Kankar, Fault classification of ball bearing by rotation forest technique, Procedia Technol., 23 (2016) 187–192.
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