基于Adaboost算法的核电站故障诊断方法 李翔宇.pdf

核动力工程
Nuclear Power Engineering
ISSN 0258-0926,CN 51-1158/TL

否可信的问题。
本文基于Adaboost算法设计了一种可使核电站控制系统自主识别故障类型的机器学习算法模型，该算法模
型通过为集成学习的各种故障识别算法合理分配权重系数，提升集成学习的整体算法对核电站事故类型的识
别精度和算法可靠性。同时测试结果表明Adaboost算法对7种典型的核电站运行或事故工况的平均识别正
确率可达95%以上；而且当事故发生150 s后，识别正确率可达100%。因此Adaboost算法对基学习器的整
合方法可用于优化集成学习的算法结构，提高算法对核电站事故类型的识别精度。
关键词：核电站瞬态运行分析；故障诊断；机器学习；Adaboost算法
中图分类号：TL363　　文献标志码：A
Fault Diagnosis Method of Nuclear Power Plant
Based on Adaboost
Li Xiangyu1, 2, Cheng Kun3, Tan Sichao1, 2*, Huang Tao3, Yuan Dongdong1, 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
Abstract: At present, most of the nuclear power plant fault diagnosis algorithms based on
ensemble learning pay attention to improving the recognition accuracy of various machine learning
algorithms, while igno