Abnormal state diagnosis model tolerant to noise in plant data

Nuclear Engineering and Technology

Seung Jun Lee¹  Jae Min Kim²  Ji Hyeon Shin² 

[1] Corresponding author.;Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan, 44919, Republic of Korea;

关键词: Accident diagnosis;    Nuclear power plant;    Abnormal operating procedure;    neural network;   

DOI  :  

来源: DOAJ

【 摘 要 】

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

【 授权许可】

Unknown