Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest
Jun Guo, Yulong Wang, Xiang Sun, Shiqiao Liu, Baigang Du
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引用次数: 0
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 imbalance data problems 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.
期刊介绍:
Nuclear Engineering and Technology (NET), an international journal of the Korean Nuclear Society (KNS), publishes peer-reviewed papers on original research, ideas and developments in all areas of the field of nuclear science and technology. NET bimonthly publishes original articles, reviews, and technical notes. The journal is listed in the Science Citation Index Expanded (SCIE) of Thomson Reuters.
NET covers all fields for peaceful utilization of nuclear energy and radiation as follows:
1) Reactor Physics
2) Thermal Hydraulics
3) Nuclear Safety
4) Nuclear I&C
5) Nuclear Physics, Fusion, and Laser Technology
6) Nuclear Fuel Cycle and Radioactive Waste Management
7) Nuclear Fuel and Reactor Materials
8) Radiation Application
9) Radiation Protection
10) Nuclear Structural Analysis and Plant Management & Maintenance
11) Nuclear Policy, Economics, and Human Resource Development