基于贝叶斯分类器的核电厂事故诊断方法研究

目前在核电厂事故诊断方面所使用的人工智能技术如神经网络等，难以同时具备较好的鲁棒性和可解释性，本研究提出基于贝叶斯分类器的核电厂事故诊断方法，并进一步将贝叶斯分类器细化为离散型朴素贝叶斯分类器、高斯型朴素贝叶斯分类器和贝叶斯网络3种，将这3种贝叶斯分类器用于核电厂事故诊断，并进行性能对比。研究结果表明：基于贝叶斯分类器的诊断方法将知识驱动和数据驱动相结合，具有较强的鲁棒性和可解释性。3种分类器中，高斯型朴素贝叶斯方法诊断在诊断准确率、诊断效率、事故破口尺寸诊断精度和事故可诊断的种类方面具有显著优势。

Research on Accident Diagnosis Method for Nuclear Power Plant Based on Bayesian Classifier

The artificial intelligence technologies currently used in nuclear power plant accident diagnosis, such as neural networks, are difficult to have both robustness and interpretability. The nuclear power plant accident diagnosis method based on Bayesian classifier was proposed in the paper, and the Bayesian classifiers were further refined into discrete naive Bayes classifiers, Gaussian naive Bayes classifiers and Bayesian networks. The performances of three Bayesian classifiers used in nuclear power plants accident diagnosis were compared. The analysis results show that the diagnosis method based on Bayesian classifier which combines knowledge driven and data driven has strong robustness and interpretability. Among the three classifiers, the Gaussian naive Bayesian method diagnosis has significant advantages in diagnosis accuracy, diagnosis efficiency, diagnosis accuracy of the size of the accident break, and types of accidents that can be diagnosed.