Application of Multi-Task Learning for Abnormal Diagnosis and Trip Variable Prediction in Nuclear Power Plants

A nuclear power plant is a large facility composed of many components, and abnormal states occasionally occur in which components fail. In the event of abnormal states, if appropriate measures are not taken, the abnormal states can worsen and cause an unexpected reactor trip. Therefore, in order to provide the operator with key state information in case of abnormal states, abnormal diagnosis and trip variable prediction were performed based on multi-task learning (MTL). The MTL is a method of performing multiple tasks through a single model. Specifically, the progressive layered extraction method, one of the MTL structures, was applied. It efficiently transmits information between tasks through a gating network and progressive routing mechanism. The proposed model showed higher diagnosis accuracy and lower prediction error than the basic MTL model. If the key state information is provided to the operator through the proposed model, it will be able to contribute to reducing human error and preventing the aggravation of abnormal states.