Online Damage Monitoring of SiC_f-SiC_m Composite Materials Using Acoustic Emission and Deep Learning

Abstract

SiCf-SiCm composites are being actively developed as fuel cladding for improving accident tolerance of light water reactor fuel. Online monitoring of the degradation process in SiCf-SiCm composites is of great importance to ensure the safety of the nuclear reactor system. The degradation monitoring task can be mapped as a classification problem: given the Acoustic Emission(AE) events at a given timeslot, the model is expected to predict which one of the following three stages the material is in: elastic, matrix-driven and fiber-driven cracking. In this paper, degradation tests on SiCf-SiCm composite tubes were conducted using a bladder-based internal pressure technique with AE monitoring. We then trained a deep learning based end-to-end convolutional neural network (CNN) model for online monitoring of the damage progression process of SiCf-SiCm composite tubes using the AE data as the raw input. As a comparison, we also applied Random Forest (RF) with expert-crafted audio event features to the damage stage prediction problem. Experimental results show that both RF and CNN models yield good results but on average our end-to-end CNN models outperform the RF models due to its high-level feature extraction capability. The CNN model with single events can reach an average prediction accuracy of 84.4% compared to 74% of the RF models. Combining multiple audio samples typically improves the accuracy of the models with RF accuracy reaching 82.8% and CNN accuracy reaching 86.6%.