A CNN-LSTM–Based Model to Fault Diagnosis for CPR1000

Abstract

With the advancement of artificial intelligence technology, intelligent diagnostic technology has been gradually implemented across various industries. This study proposes the use of convolutional neural networks–long short-term memory (CNNs-LSTM) for diagnosing faults in CPR1000 nuclear power plants (NPPs). To automatically extract data related to different types and levels of faults in the PCTRAN program, the study utilizes a self-developed AutoPCTRAN software and selects several key nuclear parameters as feature quantities. The study uses random sampling to create the training, validation, and test sets in an 8:1:1 ratio and identifies acceptable parameters to build the CNN-LSTM model. Test results show that the CNN-LSTM–based model for diagnosing CPR1000 NPP faults achieves a problem recognition rate of 99.6%, which validates the efficacy of the CNN-LSTM–based nuclear power fault diagnosis model.

Keywords:

• Nuclear power plant
• PCTRAN
• AutoPCTRAN
• CNN-LSTM
• fault diagnosis

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

We thank all the teachers in the NEAL group of the School of Nuclear Science and Technology of University of South China for their guidance and the students for their help, and the National Natural Science Foundation of China (no. 12175101), the Open Fund of State Key Laboratory (KFKT-24-2021006), and the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20220970) for their funding.