Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest

Abstract

Data-driven fault diagnosis techniques are significant for the stable operation of nuclear power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually faces the imbalance data problem with small fault samples and much redundant data which results in low model training efficiency and poor generalization performance. Thus, this paper proposes a convolutional variational autoencoding gradient-penalty Wasserstein generative adversarial network with random forest (CVGR) to reduce the impact of imbalanced samples on fault diagnosis. Firstly, a feature selection method based on the random forest is used to identify the most relevant measurements and reduce the impact of redundant data on fault diagnosis. Then, variational autoencoding is introduced into gradient-penalty Wasserstein generative adversarial to effectively extract original sample features and generate high-quality samples with high rationality and diversity. In addition, the convolutional neural network is used to extract the features of mixed samples to realize intelligent fault diagnosis. Finally, several experiments based on the Fuqing Unit 2 full-scope simulator under different operating conditions are used to validate the performance of the CVGR in data enhancement and intelligent fault diagnosis. The results show that the proposed method can effectively mitigate the imbalance data problem, which gives insights into intelligent fault diagnosis of NPPs.