A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant. (15th June 2020)

• Record Type: Journal Article
• Authors: Yao, Yuantao; Wang, Jin; Xie, Min; Hu, Liqin; Wang, Jianye
• Is Part Of: Annals of nuclear energy. Volume 141(2020)
• Publication Date: 2020-06-15
• Subjects: NPP -- Fault diagnosis -- State information imaging -- Machine learning -- Kernel principal component analysis -- F1 score; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2019.107274
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A new fault diagnosis approach with full-scope nuclear simulator is proposed. The state information imaging is used to construct the different condition images. The machine learning is employed to achieve image feature extraction and classification. The simulation result shows the high accuracy and speed of proposed fault diagnosis approach. It will be developed and assembled in the Virtual 4DS digital society environment. 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.