Long-Term Monitoring for Leaks in Water Distribution Networks Using Association Rules Mining

Early detection of small and large leaks in water distribution pipes allows for proactive maintenance and corrective actions to take place in a timely manner, thus mitigating significant water loss and increasing the longevity of the network. Most of the acoustic leak detection methods today are geared toward inspections—focused on probing periodic short-term data acquired in the process of inspection—rather than dealing with large volumes of long-term data acquired from monitoring programs. The common challenge encountered in both the acoustic inspection methods and in long-term monitoring of acoustic signatures lies in delineating weak leak-induced signatures within the highly noisy and nonstationary acoustic environment typical of uncontrolled real-world operating water distribution systems. This paper focuses on addressing the problem of leak detection where long-term monitoring acoustic data is available to characterize the operating conditions, without relying on controlled experiments to acquire data or expert user knowledge. The key contribution of this paper is to present a new data-driven approach using association rules (ARs) to extract information from large volumes of monitored acoustic data which can enable identification of relatively small changes in the acoustic signatures due to leaks. ARs are employed to model and synthesize the information contained in long-term monitored acoustic data and associations between statistical features obtained from such measurements are identified and used to design a leak indicator that captures the deviation of leak-induced data from a reference leak-free model. It will be shown that the proposed indicator has a high detection rate, can detect relatively small leaks, and crucially, conducive to work in uncontrolled long-term monitoring situations.