Water Flow Detection Device Based on Sound Data Analysis and Machine Learning to Detect Water Leakage

TL;DR

This paper presents a low-cost, easy-to-install water leak detection device that uses sound data analysis and deep learning to identify leaks by analyzing amplified water flow sounds.

Contribution

It introduces a novel, simple mechanism combining sound amplification and neural networks for effective water leak detection in building pipes.

Findings

Detects water flow as low as 100 mL/min

Uses deep neural networks for sound analysis

Easy to install on various pipe sizes

Abstract

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.