Abstract Nuclear power plant is a complex engineering system with strong coupling, nonlinearity and potential radioactive release risk. Different researchers have proposed numerous fault diagnosis techniques but there is a gap between these techniques and their practical manifestation. In this paper, kernel principal component analysis (KPCA) and similarity clustering are primarily presented for fault diagnosis. First, KPCA is utilized for anomaly detection to distinguish actual faults form abnormal sensor readings. After that, support vector machine is carried out for fault diagnosis. Subsequently, KPCA is also used for feature extraction before clustering algorithms for analyzing fault type and degree. As opposed to other ‘black box’ data-driven methods, this technique allows the results to be illustrated in a visual form which greatly enhances the interpretability of diagnosis results. Finally, the accuracy of the method is verified with a full scope NPP (Type: Pressurized Water Reactor) simulator.

中文翻译：

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基于KPCA和相似聚类的核电厂故障识别与诊断

摘要 核电站是一个复杂的工程系统，具有强耦合、非线性和潜在的放射性释放风险。不同的研究人员提出了许多故障诊断技术，但这些技术与其实际表现之间存在差距。在本文中，核主成分分析（KPCA）和相似性聚类主要用于故障诊断。首先，KPCA 用于异常检测，以区分实际故障和异常传感器读数。之后，进行支持向量机进行故障诊断。随后，KPCA 也被用于聚类算法之前的特征提取，用于分析故障类型和程度。与其他“黑匣子”数据驱动方法相反，该技术允许以可视化形式说明结果，这大大增强了诊断结果的可解释性。最后，该方法的准确性通过全范围 NPP（类型：压水反应堆）模拟器进行验证。