An improved generative adversarial network for fault diagnosis of rotating machine in nuclear power plant-Reference-Cited by-同舟云学术

An improved generative adversarial network for fault diagnosis of rotating machine in nuclear power plant

Published:2023-01 Issue: Volume:180 Page:109434
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Wang Zhichao,Xia Hong,Yin Wenzhe,Yang Bo

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference27 articles.

1. A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines;Arabaci;Int. J. Intellig. Eng. Informat.,2020

2. Support vector ensemble for incipient fault diagnosis in nuclear plant components;Ayodeji;Nucl. Eng. Technol.,2018

3. PWR heat exchanger tube defects: Trends, signatures and diagnostic techniques;Ayodeji;Prog. Nucl. Energy,2019

4. Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery;Chen;IEEE Trans. Instrum. Meas.,2020

5. Domain-adversarial training of neural networks;Ganin;J. Mach. Learn. Res.,2016

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