Recently, a research group from the Institute of Nuclear Energy Safety Technology of the Hefei Institutes of Physical Science (HFIPS) made new progress in intelligent fault diagnosis methods for complex systems of NPPs, according to Zhang Nannan of the Chinese Academy of Sciences. The scientific paper describing the system will appear in the September 2021 issue of the Annals of Nuclear Energy.

Fault diagnosis technology is an important part of the nuclear power plant operation support system, which contributes to the safety and reliability of nuclear energy production. At present, the network structure model used in fault diagnosis usually needs professional design, which is time-consuming and labour-intensive, leading to low efficiency, explained Zhang. Therefore, how to optimise the network structure of fault diagnosis and improve the efficiency and accuracy of fault diagnosis is of great significance.

To solve these problems, the paper proposes a data-driven adaptive fault diagnosis approach NSGAII-CNN. Firstly, the time-series data are mapped into two-dimensional images by Markov Transition Field, which preserves the time characteristics of the data and improves the fault diagnosis accuracy. Then, the NSGAII-CNN algorithm is proposed to realise the self-adaptive search of the network structure, which improves the construction speed of the fault diagnosis network structure model, thereby improving the diagnosis accuracy and efficiency.

Finally, compared with the current three classical CNN architecture models designed by professionals, the methodology proposed in this paper has significant advantages in fault diagnosis and model structure construction. The proposed diagnosis method will provide operators with useful information and enhance the nuclear energy systems’ self-diagnostic capabilities. The work was supported by the National Natural Science Foundation of China and the National Key R&D Programme of China.