Abstract In this paper, a new approach aimed at the Fault Diagnosis with Full-scope Simulator based on the State Information Imaging (FDFSSII) in NPP is proposed. The FDFSSII approach first constructs a series of gray-image which presents the operating transient (included normal and fault condition) according to the real time monitoring data. Furthermore, the Machine Learning (ML) technology is employed to achieve image feature extraction and classification by analyzing and learning from massive amounts of historical and synthetic gray-image data – the image feature is extracted by the Kernel Principal Component Analysis (KPCA) and classified by the designed classifiers in different learning methods. Finally, diagnosis effect is evaluated by the F1 score. The simulation result shows that the FDFSSII approach has achieved good effect for the fault diagnosis in NPP. Meanwhile, it simplifies the process of nuclear reactor with the large monitoring data and provides useful support information to the operators.

中文翻译：

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一种基于状态信息成像的核电厂全范围模拟器故障诊断新方法

摘要 本文提出了一种基于状态信息成像的全范围模拟器故障诊断新方法(FDFSSII)。FDFSSII 方法首先根据实时监测数据构建一系列灰度图像，呈现运行瞬态（包括正常和故障状态）。此外，利用机器学习（ML）技术，通过对海量历史和合成灰度图像数据的分析和学习，实现图像特征提取和分类——通过核主成分分析（KPCA）提取图像特征并分类通过不同学习方法设计的分类器。最后，通过 F1 分数评估诊断效果。仿真结果表明，FDFSSII方法在核电厂故障诊断中取得了良好的效果。同时，它通过大量的监测数据简化了核反应堆的过程，为操作人员提供了有用的支持信息。