Research on Interpretable Diagnosis Method of Reactor Accidents Based on Representation Extraction

Li Chengyuan; Li Meifu; Qiu Zhifang

doi:10.13832/j.jnpe.2023.05.0201

Volume 44 Issue 5

Oct. 2023

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Li Chengyuan, Li Meifu, Qiu Zhifang. Research on Interpretable Diagnosis Method of Reactor Accidents Based on Representation Extraction[J]. Nuclear Power Engineering, 2023, 44(5): 201-209. doi: 10.13832/j.jnpe.2023.05.0201

Citation:

Li Chengyuan, Li Meifu, Qiu Zhifang. Research on Interpretable Diagnosis Method of Reactor Accidents Based on Representation Extraction[J]. Nuclear Power Engineering, 2023, 44(5): 201-209. doi: 10.13832/j.jnpe.2023.05.0201

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Research on Interpretable Diagnosis Method of Reactor Accidents Based on Representation Extraction

doi: 10.13832/j.jnpe.2023.05.0201

Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2023-06-12
Accepted Date: 2023-06-15
Rev Recd Date: 2023-07-23
Publish Date: 2023-10-13

Abstract

Abstract

This study proposes a diagnostic framework that is interpretable and based on representation extraction to achieve accurate, robust and reliable reactor accident diagnosis. Firstly, the Denoising Padded AutoEncoder (DPAE) deep learning model is introduced. Through self-supervised learning on a simulated dataset with varying break sizes and positions, the DPAE encoder can automatically extract low-dimensional representation vectors of monitoring parameters from partially missing data and noise data, which can then be used for downstream diagnostic tasks that involve classification and regression algorithms. Then, a parameter importance calculation method based on posteriori interpretability algorithm is introduced to analyze the contribution of monitoring parameters to diagnostic results. The proposed diagnostic method is validated using HPR1000 as the research object under LOCA conditions. The experimental results show that the trained DPAE model can still obtain effective data representation under Gaussian noise with signal-to-noise ratio of 30 dB and random masking ratio of 0.3. In addition, under the interference of signal-to-noise ratio of 20 dB and masking ratio of 0.2, compared with the "end-to-end" diagnosis model, the "upstream and downstream" diagnosis model proposed in this study performs better in the diagnosis of break position and size, and can identify monitoring parameters with greater contributions to the diagnostic results. The reactor accident diagnosis method proposed in this study is helpful to build an accurate, stable and reliable intelligent reactor operation and maintenance system.
- Accident diagnosis,
- Representation extraction,
- Posterior interpretability,
- Denoising Padded AutoEncoder (DPAE)

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References(19)

References

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