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Report No.
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Acoustic detection of boiling state by deep learning; Visual explanation of identification basis

Ueki, Yoshitaka*; Hirako, Itsuki*; Aizawa, Kosuke ; Ara, Kuniaki*

With a final goal of early detection and understanding of the transition of coolant boiling events in the core of Na fast reactors, our present aim is to obtain and maintain basic knowledge necessary for developing anomaly detection technology associated with local anomalies in the core, and to demonstrate basic feasibility. We constructed a deep learning method and evaluated its performance to detect the occurrence and understand the transition of subcooled boiling using acoustic identification. In this research, we aim to visualize and understand and analyze the relationship between the temporal response of sound pressure changes according to phenomena, and acquire acoustic data that occurs during subcooled boiling of water, and learn feature quantities in time-frequency expression. A deep learning model of convolutional neural network for label classification was constructed. In addition to being able to identify the occurrence of boiling with high accuracy, the visualization of the identification basis based on Grad-CAM revealed the acoustic frequency bands that the deep learning model determined to be of high importance.

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