Diagnosis Method for Pressurized Water Reactor LOCA Accidents Based on CART-LSTM Algorithm

Sun Zhejun; Wang Yulong; Wei Xinyu; Sun Peiwei

doi:10.13832/j.jnpe.2024.080053

Volume 46 Issue 4

Aug. 2025

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Sun Zhejun, Wang Yulong, Wei Xinyu, Sun Peiwei. Diagnosis Method for Pressurized Water Reactor LOCA Accidents Based on CART-LSTM Algorithm[J]. Nuclear Power Engineering, 2025, 46(4): 212-217. doi: 10.13832/j.jnpe.2024.080053

Citation:

Sun Zhejun, Wang Yulong, Wei Xinyu, Sun Peiwei. Diagnosis Method for Pressurized Water Reactor LOCA Accidents Based on CART-LSTM Algorithm[J]. Nuclear Power Engineering, 2025, 46(4): 212-217. doi: 10.13832/j.jnpe.2024.080053

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Diagnosis Method for Pressurized Water Reactor LOCA Accidents Based on CART-LSTM Algorithm

doi: 10.13832/j.jnpe.2024.080053

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

Received Date: 2024-08-21
Rev Recd Date: 2024-10-30
Publish Date: 2025-08-15

Abstract

Abstract

Loss of Coolant Accident (LOCA) is a typical accident in pressurized water reactors, which may induce core melting. Therefore, timely diagnosis of LOCA is very important. Long Short-Term Memory (LSTM) neural network is an improved Recurrent Neural Network (RNN) that can better capture long-term dependencies in temporal data and is widely used in fault diagnosis related to temporal data. Classification and Regression Tree (CART) is a commonly used classification method, which has the characteristics of fast classification speed, high accuracy, and strong readability. Therefore, a pressurized water reactor LOCA diagnosis method based on CART-LSTM is proposed. The paper trains and optimizes the diagnostic model using the LOCA dataset, and then uses the trained model for LOCA diagnosis, thereby achieving early and rapid diagnosis of LOCA accidents. The results indicate that the diagnosis method based on CART-LSTM can accurately determine the position and specific size of LOCA accidents.
- Loss of coolant accident (LOCA),
- Fault diagnosis,
- Long short-term memory (LSTM),
- Decision tree

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References(13)

References

[1]	ZHOU G, PENG M J, WANG H. Enhancing prediction accuracy for LOCA break sizes in nuclear power plants: a hybrid deep learning method with data augmentation and hyperparameter optimization[J]. Annals of Nuclear Energy, 2024, 196: 110208. doi: 10.1016/j.anucene.2023.110208
[2]	李仕鲜,刘井泉,沈永刚. 基于神经网络方法的LOCA事故诊断[J]. 核技术,2017, 40(8): 080604.
[3]	赵云飞,张立国,童节娟,等. BP神经网络在AP1000核电站事故诊断应用中的初步研究[J]. 原子能科学技术,2014, 48(S1): 480-484.
[4]	刘永阔,夏虹,谢春丽,等. BP-RBF神经网络在核电厂故障诊断中的应用[J]. 原子能科学技术,2008, 42(3): 193-199. doi: 10.7538/yzk.2008.42.03.0193
[5]	曹桦松,孙培伟. 基于PCA-RBF神经网络的小型压水堆故障诊断方法研究[J]. 仪器仪表用户,2021, 28(1): 49-55. doi: 10.3969/j.issn.1671-1041.2021.01.013
[6]	耿柯繁,许分钦,庞波,等. 基于LSTM的核反应堆一回路传感器故障智能检测[J]. 自动化仪表,2023, 44(S1): 366-370,375.
[7]	邓志光,吴茜,朱加良,等. 基于改进LSTM的核电厂传感器故障诊断研究[J]. 自动化仪表,2023, 44(6): 115-120.
[8]	慕昱,夏虹,刘永阔. 基于邻域粗糙集和决策树算法的核电厂故障诊断方法[J]. 原子能科学技术,2011, 45(1): 44-47. doi: 10.7538/yzk.2011.45.01.0044
[9]	WANG Y Q, SUN P W. Kernel principle component analysis and random under sampling boost based fault diagnosis method and its application to a pressurized water reactor[J]. Nuclear Engineering and Design, 2023, 406: 112258. doi: 10.1016/j.nucengdes.2023.112258
[10]	SUN Z G, WANG G T, LI P F, et al. An improved random forest based on the classification accuracy and correlation measurement of decision trees[J]. Expert Systems with Application, 2024, 237: 121549. doi: 10.1016/j.eswa.2023.121549
[11]	孙运淼,林锋,周激流. 长短时记忆网络在移动场景中的应用研究进展[J]. 现代计算机,2017(35): 10-15.
[12]	DA SILVA D G, DE MOURA MENESES A A. Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction[J]. Energy Reports, 2023, 10: 3315-3334. doi: 10.1016/j.egyr.2023.09.175
[13]	ZUBAIR M, ABABNEH A, ISHAG A. Station black out concurrent with PORV failure using a Generic Pressurized Water Reactor simulator[J]. Annals of Nuclear Energy, 2017, 110: 1081-1090. doi: 10.1016/j.anucene.2017.08.023