Enhanced gate-valve failure detection in water distribution networks using ML and pressure data

This study introduces an innovative diagnostic approach for identifying gate-valve failures in water distribution systems. By implementing high-frequency pressure sensors upstream and downstream of the gate valves, we obtained detailed pressure data that are pivotal for fault diagnosis. We explored three distinct machine-learning algorithms and two data-handling techniques to ensure optimal performance in real-world applications. In our methodology, supervised learning algorithms are used to analyze pressure differentials and predict valve behavior. We rigorously tested these algorithms using both raw and feature-engineered data, and the results indicated the effectiveness of the Gaussian-naïve Bayes model with six extracted features. This approach enhances the precision and reliability of diagnostics in water distribution networks.