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Imbalanced Sample Fault Diagnosis Method for Rotating Machinery in Nuclear Power Plants Based on Deep Convolutional Conditional Generative Adversarial Network by Zhichao Wang; Hong Xia; Jiyu Zhang; Bo Yang; Wenzhe Yin is a Engineering article available to read on EtoBox.

What is Imbalanced Sample Fault Diagnosis Method for Rotating Machinery in Nuclear Power Plants Based on Deep Convolutional Conditional Generative Adversarial Network about?

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 addition, 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, base

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Author

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

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)