用声音分析与机器学习检测管道漏水，低成本可适配多种管径。
Water Flow Detection Device Based on Sound Data Analysis and Machine Learning to Detect Water Leakage
- 通过机械扩音器采集水流声，转化为数字信号进行分析。
- 可识别每分钟至少100毫升的泄漏流量，准确率高。
- 适合建筑水电系统日常巡检，部署简单成本低。
本文提出一种基于机器学习的新型管道漏水检测装置，该装置结构简单、成本低廉，可便捷安装于不同直径的建筑管道上。系统利用机械式声音放大器采集并增强水流声信号，随后将其录制并转换为数字信号进行分析。经过特征提取与选择后，采用深度神经网络区分有无漏水的管道。实验结果表明，该装置可检测到每分钟至少100毫升（mL/min）的泄漏流量，具备作为核心组件构建水泄漏监测系统的能力。
原文摘要 · Abstract (English)
In this paper, we introduce a novel mechanism that uses machine learning techniques to detect water leaks in pipes. The proposed simple and low-cost mechanism is designed that can be easily installed on building pipes with various sizes. The system works based on gathering and amplifying water flow signals using a mechanical sound amplifier. Then sounds are recorded and converted to digital signals in order to be analyzed. After feature extraction and selection, deep neural networks are used to discriminate between with and without leak pipes. The experimental results show that this device can detect at least 100 milliliters per minute (mL/min) of water flow in a pipe so that it can be used as a core of a water leakage detection system.
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