Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems

Pressure transients have been identified as one of the major contributing factors in many pipe failures in water distribution systems (WDSs). The behavior of these pressure transients is largely unknown and cannot be fully assessed by numerical simulation or modeling. This study investigates the behavior of pressure transients in WDSs as measured by high-frequency pressure sensors. A Time Series Data Mining (TSDM) approach is proposed to detect and cluster pressure transients to reveal recurrent and consistent patterns. The proposed technique, based on a modified two-sided cumulative sum (CUSUM) algorithm, is used to detect pressure transients. Dynamic Time Warping (DTW) is adopted to measure the similarity between the detected pressure transients, and k-means clustering algorithm is used to discover the characteristic patterns. Several performance scores are suggested to evaluate the quality of the clustering results, including sum of squared error, Silhouette index, and Calinski-Harabaz index. Results demonstrate that the proposed approach is able to reveal consistent and unique patterns across multiple sensing locations. The proposed approach provides a fast and efficient way to discover the hidden information in WDSs by analyzing high-frequency pressure signals from distributed sensors.