A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder-Reference-Cited by-同舟云学术

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A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder

Published:2020-11 Issue:11 Volume:34 Page:4367-4381
ISSN:1738-494X
Container-title:Journal of Mechanical Science and Technology
language:en
Short-container-title:J Mech Sci Technol

Author:

Aljemely Anas H.,Xuan Jianping,Jawad Farqad K. J.,Al-Azzawi Osama,Alhumaima Ali S.

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://link.springer.com/content/pdf/10.1007/s12206-020-1002-x.pdf

Reference47 articles.

1. H. Jiang, C. Li and H. Li, An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis, Mech. Syst. Signal Process., 36(2) (2013) 225–239.

2. J. Chen et al., Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review, Mech. Syst. Signal Process., 70–71 (2016) 1–35.

3. F. Jia, Y. Lei, J. Lin, X. Zhou and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process., 72–73 (2016) 303–315.

4. X. Jin, M. Zhao, T. W. S. Chow and M. Pecht, Motor bearing fault diagnosis using trace ratio linear discriminant analysis, IEEE Trans. Ind. Electron., 61(5) (2014) 2441–2451.

5. H. Wang and P. Chen, Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network, Comput. Ind. Eng., 60(4) (2011) 511–518.

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