Aging detection of plant control system components using recurrent neural network

A control system detects the aging faults of instruments on the basis of whether the signal is outside of a pre-defined range. Potential adverse effects to plant safety caused by instrument aging increase until triggering the set-point. Detecting an aging fault early can contribute to the safe operation of a nuclear plant. Recently, the recurrent neural network (RNN) has performed well in analyzing time-series data. This study proposes a mechanism for aging detection using long short-term memory (LSTM), which is an improved model of a RNN. The mechanism consists of three phases, input preprocessing, the LSTM network, and output evaluation. The LSTM is trained with a sequential dataset assembled through preprocessing of raw data logged in a compact nuclear simulator. Output processing includes classification to distinguish a normal or aging state from the output of the LSTM and evaluation of the diagnosis results. The experiment results show that the proposed approach provides stable detection capability.