Fault Diagnosis Method of Nuclear Power Plant Based on Adaboost Algorithm
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摘要: 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。
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关键词:
- 核电站瞬态运行分析 /
- 故障诊断 /
- 机器学习 /
- Adaboost算法
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. -
图 1 6种事故工况反应堆热功率和电功率随时间变化曲线
Figure 1. Changing Curve of Reactor Thermal Power and Electric Power with Time in Six Accident Conditions
图 2 4种机器学习算法识别7种核电站运行工况的接收者操作特征曲线
Figure 2. The Receiver Operating Characteristic Curve of Each Operating Condition When Four Machine Learning Algorithms are Used to Identify Seven Operating Conditions of Nuclear Power Plant
图 3 算法模型的AUC柱状图与识别所消耗的时间图
Figure 3. The AUC Histogram of the Algorithm Model and the Time Consumed for Identification
图 4 Adaboost算法模型对于4种时间连续的瞬态运行参数对应运行工况的识别结果图
Figure 4. The Pictures of the Identification Results of Adaboost Algorithm Model for Four Kinds of Time Continuous Transient Operation Parameters Corresponding to the Operation Conditions
图 5 Adaboost算法在各时刻对故障类型的识别精度曲线图
Figure 5. The Curve Chart of Fault Type Identification Accuracy of Adaboost Algorithm at Each Time
表 1 Adaboost算法对各种运行工况识别的混淆矩阵和精度表
Table 1. The Confusion Matrix and Precision Table of Adaboost Algorithm for Identification of Various Operating Conditions
运行工况 真正例 假反例 假正例 真反例 识别正确率/% 工况1 822 178 0 6000 97.45 工况2 766 234 116 5884 95.00 工况3 913 87 77 5923 97.65 工况4 773 227 68 5932 95.79 工况5 737 263 1 5999 96.23 工况6 737 265 6 5994 96.12 工况7 922 78 44 5956 98.26 
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