Towards AI-Based Condition Monitoring and Predictive Maintenance for Water Smart Pipes: The SANDMAN Approach

Abstract

Pipes age and corrosion are the main factors of leakage in water distribution networks. According to the World Resources Institute, European countries will face water problems by 2040. If we take Italy as an example, more than 40% of drinking water was lost in 2020 due to leaky aqueducts. Decrepit pipes can lead to environmental concerns, economical losses, and potential public health problems if water gets contaminated. Localizing leakage positions in an accurate way is often a big challenge. On the other side, replacing decrepit pipes is not an easy task and usually costly. An optimal solution to deal with water leakage is to use smart pipes where appropriate sensors monitoring the conditions of the pipes are incorporated in. Digitalization plays a crucial role here. By providing accurate information about the pipes, and use artificial intelligence techniques for data analysis, potential leakages and their corresponding positions can be detected in time, which allows to schedule a maintenance task as soon as possible. The current paper discusses the use of smart pipes combined with predictive maintenance and shows how this combination improves water leakage detection, hence minimizing water waste, and protecting the environment. The solution was validated in an experimental setup put in place by the Italian company EKSO s.r.l in its factory facilities in Rozallo, Italy. The obtained results show the feasibility of the solution and the relevance of using artificial intelligence techniques for predicting degradation in smart pipes.