Sound Recognition and Early Warning Mechanism for Liquid Aluminum Leakage Based on Improved EfficientNetV2

Liquid aluminum leakage is the direct cause of explosion accidents in aluminum deep-well casting processes. To address the practical engineering challenges of strong lag, low accuracy, and limited monitoring range in existing leakage detection methods, this paper proposed a sound recognition method for liquid aluminum leakage based on an improved EfficientNetV2 model. This method utilizes acoustic characteristics to identify leaks, thereby expanding the monitoring range. The core enhancement involves optimizing the stacking factor and integrating an efficient channel attention mechanism into the EfficientNetV2 architecture to further improve the recognition speed and accuracy. Firstly, a sound database encompassing seven types of acoustic scenes was constructed by collecting audio data under different scenarios using pickups. Then, log-Mel spectrograms were extracted from the sound signal as the feature set and fed into the improved EfficientNetV2 model for training and validation, finally yielding the liquid aluminum leakage sound recognition model. The experimental results show that the recognition accuracy of the improved EfficientNetV2 reaches 95.48%. Compared to the original EfficientNetV2, ResNet, RegNet and DenseNet, the proposed model requires only 12.34%, 8.64%, 11.14%, and 10.80% of the floating point operations, and 11.37%, 9.55%, 15.95%, and 17.24% of the parameters, respectively. Furthermore, it processes 6.53, 6.14, 4.41, and 8.00 times more frames per second in a CPU environment, confirming its fast and accurate recognition performance. In addition, a risk early-warning mechanism for liquid aluminum leakage was established based on the proposed sound recognition method and deployed for real-time risk monitoring in a casting unit. Practical application results verify the effectiveness of both the identification method and the warning mechanism, providing a valuable technical reference for preventing explosion accidents in aluminum deep-well casting.