Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks-Reference-Cited by-同舟云学术

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Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks

Published:2021-08 Issue: Volume:180 Page:109553
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Liu Yong Zhi,Shi Ke Ming,Li Zhi Xuan,Ding Guo Fu,Zou Yi Sheng

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

Reference30 articles.

1. A review on data-driven fault severity assessment in rolling bearings;Cerrada;Mech. Syst. Signal Process.,2018

2. A review on empirical mode decomposition in fault diagnosis of rotating machinery;Lei;Mech. Syst. Signal Process.,2013

3. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network;Zhang;J. Intell. Manuf.,2013

4. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics;Qiu;J. Sound Vib.,2006

5. An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing;Guo;Appl. Acoust.,2017

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