CNN-based acoustic identification of gas-liquid jet: Evaluation of noise resistance and visual explanation using Grad-CAM

For the analysis of anomalies in a steam generator (SG) of a sodium-cooled fast reactor (SFR), we evaluate the noise resistance of CNN-based acoustic identification methods of gas-liquid two-phase jets and produce visual explanations for their decisions. First, we introduce the water flow sound and the three types of gas-liquid jet sounds, which simulate the background noise and the anomaly sounds, respectively. Second, we produce time-frequency representations for various signal-to-noise ratios (SNRs) and employ AlexNet, VGG16, and ResNet18 to the identification of the gas-liquid two-phase jets. As a result, the best CNN of ResNet18 achieves more than 92% for SNR = 0, -4, -8, and -12 dB and 69% for SNR = -16 and -20 dB. This result indicates that our proposed methods can identify the flow states of gas-liquid two-phase jets in low-level noise environments and detect the gas-liquid two-phase jets even in high-level noise environments. Also, Grad-CAM suggests that ResNet18 focuses on one of the spectrum peaks of the water flow sound and all or part of the signal intensity pattern of the gas-liquid jet sounds. Our proposed methods lead to the safe operation and fast, accurate, and accountable analysis of anomalies in SFR.