基于Adaboost算法的核电站故障诊断方法

针对当前基于集成学习的核电站故障诊断算法大多注重提高各种机器学习算法识别精度而忽略底层基学习器整合方法，导致集成学习算法识别事故类型精度难以提高，而且存在识别结果是否可信的问题。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%。因此基于Adaboost算法的基学习器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。 

Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm

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.