BOILING SENSING BASED ON ACOUSTIC RECOGNITION AND DEEP LEARNING

Abstract

Anomaly detection in nuclear power plants enables us to early execute prevention and mitigation measures against accident progression. In sodium-cooled fast reactors, coolant boiling in reactor cores is one of the important phenomena in the safety assessment. Our final target of the present study is to realize the acoustic anomaly detection of the boiling inception in actual reactors. In the actual environment, various sorts of noises are expectedly superposed on accidental boiling sounds. It is inevitable to distinguish the boiling sounds from the superimposing hostile disturbance with high accuracy. To achieve this, we utilize machine learning techniques and assess the feasibility of boiling sensing based on acoustic recognition and deep learning. In the present study, we employ an autoencoder to denoise boiling sounds, and a convolutional neural network to detect the boiling inception. The boiling acoustics have not been fully understood yet. In the present study, we find that some characteristics of the boiling acoustics are consistent with the resonance vibration of the heating body. This finding contributes to elucidating the physics of boiling acoustics. In addition, it helps us detect boiling occurrences with high accuracy judging from the acoustic characteristics’ patterns.