Fault diagnosis method of rolling bearings under varying working conditions based on deep feature transfer-Reference-Cited by-同舟云学术

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Fault diagnosis method of rolling bearings under varying working conditions based on deep feature transfer

Published:2020-11 Issue:11 Volume:34 Page:4383-4391
ISSN:1738-494X
Container-title:Journal of Mechanical Science and Technology
language:en
Short-container-title:J Mech Sci Technol

Author:

Kang Shouqiang,Qiao Chunyang,Wang Yujing,Wang Qingyan,Hu Mingwu,Mikulovich V. I.

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://link.springer.com/content/pdf/10.1007/s12206-020-1003-9.pdf

Reference21 articles.

1. S. T. Wan and B. Peng, The FERgram: a rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio, Journal of Mechanical Science and Technology, 33(1) (2019) 157–172.

2. H. M. Zhao, H. L. Liu, J. J. Xu, C. Guo and W. Deng, Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network, Journal of Mechanical Science and Technology, 33(9) (2019) 4165–4172.

3. W. Zhang, C. H. Li and G. L. Peng, A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment different working load, Mechanical Systems and Signal Processing, 100 (2018) 439–453.

4. J. Park, M. Hamadache and J. M. Ha, A positive energy residual (PER) based planetary gear fault detection method under variable speed conditions, Mechanical Systems and Signal Processing, 117 (2019) 347–360.

5. M. Cocconcelli, L. Bassi and C. Secchi, An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles, Mechanical Systems and Signal Processing, 27(1) (2012) 667–682.

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