Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network-Reference-Cited by-同舟云学术

Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network

Published:2020-07 Issue: Volume:159 Page:107768
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Liang Pengfei,Deng Chao,Wu Jun,Yang Zhixin

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

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

Reference45 articles.

1. Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery;Zhao;Measurement.,2020

2. Q. Lu, R. Yang, M. Zhong, Y. Wang, An improved fault diagnosis method of rotating machinery using sensitive features and RLS-BP neural network, IEEE Trans. Instrum. Meas., (2019) 1–9. https://doi.org/10.1109/tim.2019.2913057(2019).

3. An intelligent fault diagnosis model for rotating machinery based on multi-scale higher order singular spectrum analysis and GA-VPMCD;Luo;Measurement.,2016

4. Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method;Zhang;Measurement.,2020

5. B. Yang, Z. Yang, R. Sun, Z. Zhai, Fast nonlinear chirplet dictionary-based sparse decomposition for rotating machinery fault diagnosis under nonstationary conditions, IEEE Trans. Instrum. Meas., (2019) 1–10. https://doi.org/10.1109/tim.2019.2900886(2019).

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