基于朴素贝叶斯的核电厂事故诊断方法研究

本文将机器学习领域的贝叶斯技术应用于核应急中的电厂状态诊断,提出了基于朴素贝叶斯分类器的核电厂事故诊断方法。利用压水堆核电厂仿真机获取事故案例数据,对朴素贝叶斯分类模型进行训练,实现了对核电厂多类事故(LOCA、SGTR、MSLB)的诊断。测试结果表明,基于朴素贝叶斯分类器的核电厂事故诊断方法在诊断精度、诊断效率、事故类型可扩展性以及程序自主化诊断上有显著优势,并且模型训练中不同事故类型先验分布对诊断结果影响较小,具有较好的适用性。

Research on fault diagnosis methods for nuclear power plant based on Naive Bayes

This paper introduces Bayesian techniques from the machine learning field into the application of power plant accident diagnosis in nuclear emergency. A new approach for plant accident diagnosis based on Naive Bayes Classifier is proposed. The PWR nuclear power plant simulator is used to obtain accident case data, and the naive Bayes classification model is trained to realize the diagnosis of multiple types of accidents (LOCA, SGTR, MSLB) in nuclear power plants. The test results show that the accident diagnosis methods based on naive Bayesian classifier have significant advantages in diagnostic accuracy, diagnostic efficiency, expandability of accident types and program autonomy diagnosis. It is found the prior distribution of different accident types in model training has little influence on the training performance, indicating the good applicability of the new approach.