Fault Detection in Nuclear Power Plants using Deep Leaning based Image Classification with Imaged Time-series Data
DOI:
https://doi.org/10.15837/ijccc.2022.1.4714Keywords:
fault detection, nuclear power plants, deep learning, image classification, imaged time-series dataAbstract
Fault detection is critical to ensure the safely routine operations in nuclear power plants (NPPs), requiring very high accuracy and efficiency. Meanwhile, the rapid development of modern information technologies have profoundly changed and promoted various sectors including nuclear industry. Inspired by the great progress and promising performance of deep learning based image classification recent years, a two-stage fault detection methodology in NPPs has been proposed in this paper. First the time-series data describing the operating status of NPPs have been transformed into two-dimensional images by four methods, preserving the time-series information in images and converting the fault detection problem into a supervised image classification task. Then four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data, achieving excellent accuracies. Further the performances of different combinations of transforming means and classifying models have been compared and discussed with extensive experiments and detailed analysis of throughput for four transforming methods. This methodology proposed has obtained remarkable results by reshaping data format and structure, making image classifying models applicable, which not only efficiently detect and warn possible faults in NPPs but also enhances the capability for safety management in nuclear power systems.
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[31] Mo, K.; Lee, S. J.; Seong, P.H. (2007). A dynamic neural network aggregation model for transient diagnosis in nuclear power plants, Progress in Nuclear Energy, 49, 262-272, 2007. https://doi.org/10.1016/j.pnucene.2007.01.002
[32] Mohapatra, D.; Subudhi, B.; Daniel, R. (2020). Real-time sensor fault detection in Tokamak using different machine learning algorithms, Fusion Engineering and Design, 151, 111401, 2020. https://doi.org/10.1016/j.fusengdes.2019.111401
[33] Nicolau, A. D.; Augusto, J. P.; Schirru, R. (2017). Accident diagnosis system based on real-time decision tree expert system, AIP Conference Proceeding, 1836, 020017, 2017. https://doi.org/10.1063/1.4981957
[34] Oh, C.; Lee, J. I. (2020). Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network, Reliability Engineering and System Safety, 198, 106879, 2020. https://doi.org/10.1016/j.ress.2020.106879
[35] Po, L. (2019). Conceptual Design of an Accident Prevention System for Light Water Reactors Using Artificial Neural Network and High-Speed Simulator, Nuclear Technology, 206, 505-513, 2019. https://doi.org/10.1080/00295450.2019.1641877
[36] Racheal, S.; Liu, Y.; Ernest, M.; Ayodeji, A. (2021). A Systematic Review of PCTRAN-Based Pressurized Water Reactor Transient Analysis, Proceedings of the 2021 28th International Conference on Nuclear Engineering, 2021. https://doi.org/10.1115/ICONE28-65663
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