A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant

In this paper, a new approach aimed at the Fault Diagnosis with Full-scope Simulator based on the StateInformation Imaging (FDFSSII) in NPP is proposed. The FDFSSII approach first constructs a series of grayimagewhich presents the operating transient (included normal and fault condition) according to the realtime monitoring data. Furthermore, the Machine Learning (ML) technology is employed to achieve imagefeature extraction and classification by analyzing and learning from massive amounts of historical andsynthetic 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 effectis evaluated by the F1 score. The simulation result shows that the FDFSSII approach has achieved goodeffect for the fault diagnosis in NPP. Meanwhile, it simplifies the process of nuclear reactor with the largemonitoring data and provides useful support information to the operators.