Imbalanced sample fault diagnosis method for rotating machinery in nuclear power plants based on deep convolutional conditional generative adversarial network

Wang Zhichao; Xia Hong; Zhang Jiyu; Yang Bo; Yin Wenzhe

doi:10.1016/j.net.2023.02.036

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자료유형: 학술저널

저자정보: Wang Zhichao (Harbin Engineering University) Xia Hong (Harbin Engineering University) Zhang Jiyu (Harbin Engineering University) Yang Bo (Harbin Engineering University) Yin Wenzhe (Harbin Engineering University)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제55권 제6호

발행연도: 2023.6

수록면: 2,096 - 2,106 (11page)

DOI: 10.1016/j.net.2023.02.036

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Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addi tion, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs

#Nuclear power plant #Imbalanced sample fault diagnosis #Deep convolutional neural network #Conditional generative adversarial network #Rotating machinery

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