Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm

Li Xiangyu; Cheng Kun; Tan Sichao; Huang Tao; Yuan Dongdong

doi:10.13832/j.jnpe.2022.04.0118

Volume 43 Issue 4

Aug. 2022

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2022 > 43(4): 118-125

Li Xiangyu, Cheng Kun, Tan Sichao, Huang Tao, Yuan Dongdong. Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm[J]. Nuclear Power Engineering, 2022, 43(4): 118-125. doi: 10.13832/j.jnpe.2022.04.0118

Citation:

Li Xiangyu, Cheng Kun, Tan Sichao, Huang Tao, Yuan Dongdong. Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm[J]. Nuclear Power Engineering, 2022, 43(4): 118-125. doi: 10.13832/j.jnpe.2022.04.0118

Citation:

PDF( 4122 KB)

Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm

doi: 10.13832/j.jnpe.2022.04.0118

Li Xiangyu^{1, 2
,},
Cheng Kun³,
Tan Sichao^{1, 2
,
,},
Huang Tao³,
Yuan Dongdong^{1, 2}

1.
Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment, Harbin, 150001, China
2.
College of Nuclear Science and Technology, Harbin Engineering University, Harbin, 150001, China
3.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2021-08-09
Rev Recd Date: 2021-09-07
Publish Date: 2022-08-04

Abstract

Abstract

At present, most of the nuclear power plant fault diagnosis algorithms based on ensemble learning pay attention to improving the identification accuracy of various machine learning algorithms, while ignoring the integration method of the underlying base learner, which makes it difficult to improve the accuracy of the ensemble learning algorithm in identifying accident types, and there is a problem of whether the identification results are credible. In this paper, based on Adaboost algorithm, a machine learning algorithm model is designed to enable the control system of a nuclear power plant to identify fault types independently. By reasonably allocating weight coefficients for various fault identification algorithms of ensemble learning, the algorithm model improves the identification accuracy and reliability of the whole ensemble learning algorithm for nuclear power plant accident types. At the same time, the test results show that the average identification accuracy of Adaboost algorithm for seven typical nuclear power plant operation or accident conditions can reach more than 95%; And when the accident occurs 150 seconds, the identification accuracy can reach 100%. Therefore, the integration method of Adaboost algorithm to the base learner can be used to optimize the algorithm structure of ensemble learning and improve the identification accuracy of the algorithm for the types of nuclear power plant accidents.
- Transient operation analysis of nuclear power plant,
- Fault diagnosis,
- Machine learning,
- Adaboost algorithm

FullText(HTML)

References(17)

References

[1]	HOLMBERG J E, KAHLBOM U. Application of human reliability analysis in the deterministic safety analysis for nuclear power plants[J]. Reliability Engineering & System Safety, 2020, 194: 106371.
[2]	EEM S, CHOI I K, CHA S L, et al. Seismic response correlation coefficient for the structures, systems and components of the Korean nuclear power plant for seismic probabilistic safety assessment[J]. Annals of Nuclear Energy, 2021, 150: 107759. doi: 10.1016/j.anucene.2020.107759
[3]	CHOUDRI M Z. AP1000非能动安全壳冷却系统整合确定论与概率论安全分析[D]. 北京: 华北电力大学（北京）, 2019.
[4]	张力,王春波,邹衍华,等. 核电厂安全相关人员行为确定论筛选方法研究[J]. 安全与环境学报,2018, 18(5): 1854-1858.
[5]	黄志超,初永越,李虎伟,等. 风险指引型PSA应用的不确定性分析方法研究[J]. 核科学与工程,2017, 37(5): 830-838. doi: 10.3969/j.issn.0258-0918.2017.05.019
[6]	SONG G S, KIM M C. Mathematical formulation and analytic solutions for uncertainty analysis in probabilistic safety assessment of nuclear power plants[J]. Energies, 2021, 14(4): 929. doi: 10.3390/en14040929
[7]	ČEPIN M. Application of shutdown probabilistic safety assessment[J]. Reliability Engineering & System Safety, 2018, 178: 147-155.
[8]	LEE H, JEONG H, KOO G, et al. Attention recurrent neural network-based severity estimation method for interturn short-circuit fault in permanent magnet synchronous machines[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3445-3453. doi: 10.1109/TIE.2020.2978690
[9]	XING S B, LEI Y G, WANG S H, et al. Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions[J]. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2617-2625. doi: 10.1109/TIE.2020.2972461
[10]	LI J K, LIN M. Ensemble learning with diversified base models for fault diagnosis in nuclear power plants[J]. Annals of Nuclear Energy, 2021, 158: 108265. doi: 10.1016/j.anucene.2021.108265
[11]	ZHAO Y F, TONG J J, ZHANG L G. Rapid source term prediction in nuclear power plant accidents based on dynamic bayesian networks and probabilistic risk assessment[J]. Annals of Nuclear Energy, 2021, 158: 108217. doi: 10.1016/j.anucene.2021.108217
[12]	RÄTSCH G, ONODA T, MÜLLER K R. Soft margins for AdaBoost[J]. Machine Learning, 2001, 42(3): 287-320. doi: 10.1023/A:1007618119488
[13]	MERLER S, CAPRILE B, FURLANELLO C. Parallelizing AdaBoost by weights dynamics[J]. Computational Statistics & Data Analysis, 2007, 51(5): 2487-2498.
[14]	艾鑫,刘永阔,蒋利平,等. 基于iForest-Adaboost的核电厂一回路故障诊断技术研究[J]. 核动力工程,2020, 41(3): 208-213.
[15]	SRA S, NOWOZIN S, WRIGHT S J. Optimization for machine learning[M]. Cambridge: MIT Press, 2012.
[16]	QI K L, LI Q, LIU W, et al. Qinshan 300MWe NPP full scope simulator upgrade[R]. Vienna: International Atomic Energy Agency, 2006.
[17]	FAN J, UPADHYE S, WORSTER A. Understanding receiver operating characteristic (ROC) curves[J]. Canadian Journal of Emergency Medicine, 2006, 8(1): 19-20. doi: 10.1017/S1481803500013336