Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings-Reference-Cited by-同舟云学术

Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings

Published:2017-10-09 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Islam M. M. Manjurul,Kim Jong-Myon^ORCID

Funder

Korea Institute of Energy Technology Evaluation and Planning

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s12652-017-0585-2.pdf

Reference29 articles.

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4. Chen X, Zhou J, Xiao J, Zhang X, Xiao H, Zhu W, Fu W (2014) Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings. Appl Math Comput 247:835–847. doi: 10.1016/j.amc.2014.09.062

5. Chih-Wei H, Chih-Jen L (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Networks 13:415–425. doi: 10.1109/72.991427

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