In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment. Based on recordings from seven contact microphones attached to the water supply network of a municipal suburb, we trained several shallow and deep anomaly detection models. Inspired by how human experts detect leaks using electronic sounding-sticks, we use these models to repeatedly listen for leaks over a predefined decision horizon. This way we avoid constant monitoring of the system. While we found the detection of leaks in close proximity to be a trivial task for almost all models, neural network based approaches achieve better results at the detection of distant leaks.
@article{arxiv.2012.06280,
title = {Acoustic Leak Detection in Water Networks},
author = {Robert Müller and Steffen Illium and Fabian Ritz and Tobias Schröder and Christian Platschek and Jörg Ochs and Claudia Linnhoff-Popien},
journal= {arXiv preprint arXiv:2012.06280},
year = {2021}
}