Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant

Abstract

A condition-based maintenance (CBM) regime in a nuclear plant will result in eliminating unnecessary maintenance cost without jeopardizing the safety of the plant. The foundation of a good CBM regime is an accurate and timely fault detection. A method has been developed to identify transients and detect fault in a Nuclear power plant in transients. This is to aid condition-based maintenance in a nuclear power plant. This method was achieved by using the nuclear plant simulator as a dynamic reference. At steady state, a fault is easily detected but in transients, it is difficult. This gives rise to the introduction of a machine-learning tool like artificial neural networks (ANN) to train both the simulator and plant parameters. The neural network outputs of the plant and simulator are then compared and this results in a better identification of faults in transients.