A distributed analysis of vibration signals for leakage detection in Water Distribution Networks

Abstract

Abstract. It is well known that Water Distribution Networks (WDNs) are very inefficient and, in Italy, 40% of water is lost during distribution. In this paper, we present a solution for detecting leakages in WDNs, based on three main components: i) an innovative sensing element to be deployed at the sensor nodes, which analyses vibrations in the acoustic range for classifying external noise sources, induced by water leakages, by means of suitable machine learning techniques; ii) an Internet of Things (IoT) system of sensors, deployed at the junctions of the WDNs, for comparing the measurements collected at different critical points of the network; iii) a machine learning algorithm for processing the data. After the definition of the WDN structure, we introduce some numerical simulation tools suitable for studying our system and modeling the proposed sensing solution. Given the geometry, physical properties (pipe lengths, diameters, roughness, reservoir shapes and levels, pump and valve characteristic curves) and nodal demands, the simulation tool is able to compute leakages in pipes or nodes over time. In parallel, we simulate our IoT system coupled to the WDN, by logging partial information about the WDN status, which corresponds to the demand readings at the edge nodes or at some junction nodes, together with the (optional) measurements of the deployed sensing elements. On the basis of this data, we analyze the possibility of identifying the leakages in the network, even without knowing the exact or complete topology of the WDN. Our solution exploits different machine learning techniques devised to indirectly retrieve topological information, by correlating the balance of the flows as the water demand varies over time.

Vibration-based water leakage detection system for public open data platforms

“…Among the many accelerations sensors available, the selection focused on MEMS accelerometers, which are known for their small size, low costs, and their ability to sense over a relatively large bandwidth, starting from very low frequencies. This approach has proven to be promising in (Khulief, et al, 2012), (Mistretta, et al, 2018), (Restuccia, et al, 2023) and(Lo Valvo, et al, 2023).…”