Acoustic Leak Detection in Water Networks
This work addresses the problem of energy-efficient and easily deployable leak detection for municipal water suppliers, offering an incremental improvement in detecting distant leaks.
This paper presents an acoustic leak detection procedure for water networks using recordings from seven contact microphones. The authors trained shallow and deep anomaly detection models, finding that neural networks performed better at detecting distant leaks compared to other models.
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.