Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks-Reference-Cited by-同舟云学术

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Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

Published:2018-05-25 Issue:20 Volume:22 Page:6719-6729
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Appana Dileep K.,Prosvirin Alexander,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

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/article/10.1007/s00500-018-3256-0/fulltext.html

Reference32 articles.

1. Ahmed HOA, Wong MLD, Nandi AK (2016) Effects of deep neural network parameters on classification of bearing faults. In: IECON 2016—42nd annual conference of the IEEE Industrial Electronics Society, 23–26 Oct 2016. pp 6329–6334. https://doi.org/10.1109/IECON.2016.7793957

2. Appana DK, Islam MR, Kim J-M (2017) Reliable fault diagnosis of bearings using distance and density similarity on an enhanced k-NN. In: Australasian conference on artificial life and computational intelligence. Springer, pp 193–203

3. Chen Z, Li C, Sanchez R-V (2015) Gearbox fault identification and classification with convolutional neural networks. Shock Vib 2015:1–10. https://doi.org/10.1155/2015/390134

4. Chen Z, Zeng X, Li W, Liao G (2016) Machine fault classification using deep belief network. In: 2016 IEEE international instrumentation and measurement technology conference proceedings, 23–26 May 2016. pp 1–6. https://doi.org/10.1109/I2MTC.2016.7520473

5. Christian Gerber MC (2017) Number plate detection with a multi-convolutional neural network approach with optical character recognition for mobile devices. J Inf Process Syst 12:100–108. https://doi.org/10.3745/JIPS.04.0022

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