A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions-Reference-Cited by-同舟云学术

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A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions

Published:2019-10 Issue: Volume:145 Page:94-107
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Qiu Guangqi,Gu Yingkui,Cai Quan

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province in China

Education Department of Jiangxi Province in China

Research Foundation

Jiangxi University of Science and Technology in China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference43 articles.

1. Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox;Zhang;J. Sound Vib.,2018

2. D. Scherer, A. Müller, S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, In Artificial Neural Networks–ICANN 2010 (pp. 92-101), Springer, Berlin, Heidelberg.

3. Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information;Yu;J. Sound Vib.,2018

4. Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis;Li;Measurement,2018

5. An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions;Chen;Mech. Syst. Sig. Process.,2018

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