| Title | Abnormality Diagnosis in NPP Using Artificial Intelligence Based on Image Data |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Lee, Sang Hyun, Oh, Sang Won, Jo, Hye Seon, Na, Man Gyun |
| Conference Name | 2021 5th International Conference on System Reliability and Safety (ICSRS) |
| Keywords | compositionality, convolution, convolutional neural network, convolutional neural networks, Deep Learning, Diagnosis, expandability, image data, Image resolution, Numerical models, pubcrawl, Recurrent neural networks, reliability, Resiliency, Safety |
| Abstract | Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data. |
| DOI | 10.1109/ICSRS53853.2021.9660731 |
| Citation Key | lee_abnormality_2021 |
Abnormality Diagnosis in NPP Using Artificial Intelligence Based on Image Data