华南理工大学学报（自然科学版）2026，Vol.54Issue(2)：38-51,14.DOI:10.12141/j.issn.1000-565X.250006
基于改进EfficientNetV2的铝液泄漏声音识别与预警机制
Sound Recognition and Early Warning Mechanism for Liquid Aluminum Leakage Based on Improved EfficientNetV2
摘要
Abstract
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.关键词
铝加工深井铸造/铝液泄漏/声音识别/风险预警/改进的EfficientNetV2/对数梅尔语谱图Key words
aluminum deep-well casting/liquid aluminum leakage/sound recognition/risk early warning/improved EfficientNetV2/logarithmic Mel-spectrogram分类
信息技术与安全科学引用本文复制引用
梁艳辉,温承杰,闫军威,周璇,张洪涛..基于改进EfficientNetV2的铝液泄漏声音识别与预警机制[J].华南理工大学学报（自然科学版）,2026,54(2):38-51,14.基金项目
广东省技术委托开发项目(2022440002001110)Supported by the Guangdong Provincial Technology Commission Development Project(2022440002001110) （2022440002001110）
