基于多任务卷积神经网络的供水管道泄漏识别和定位方法

Leak detection and localization using multi-target convolutional neural networks

针对传统地面声学技术过度依赖工人经验的弊端,基于多通道信号的多任务卷积神经网络(MTCNN),开发了应用该模型的无线多探头漏损定位仪。多任务卷积神经网络模型模型结合无线多探头漏损定位仪,能够地面上探测泄漏并且定位漏点位置。在实际管道的应用结果表明,所提出的方法的识别准确率达98.63%,定位的平均误差为0.2 m,效果显著。

Ground acoustics technology is a widely used and effective method for detecting water leakage in supply pipe networks.To address the limitation of traditional ground acoustics technology,which overly relies on the expertise of workers,this study developed a wireless multi-probe leak locator based on the multi-task convolutional neural network(MTCNN) with multi-channel signals.The MTCNN model,combined with the wireless multi-probe leak locator,can detect leaks on the ground and locate the leak points.The application results in actual pipelines show that the proposed method achieves a recognition accuracy of 98.63%,with an average location error of 0.2 meters,demonstrating significant effectiveness.