Fixed-Interval Sampling for Water Distribution System Monitoring: An Adaptive Strategy through Time-Frequency Analysis

Sensors in water distribution systems (WDSs) generally use fixed-interval sampling, such as once every 15 min; however, whether this sampling frequency meets actual monitoring and modeling needs has not been sufficiently explored. This study continuously collected pressure monitoring data from three locations in a WDS at a 100-Hz sampling frequency for one month. Frequency analysis reveals that the signal is nonstationary, with frequency characteristics varying significantly across different periods of the day. Since the information content (e.g., high-frequency details) conveyed by the signal differs across time, this necessitates time-varying sampling requirements to adequately capture critical data in each period. Based on these findings, an adaptive sampling method (ASM) for WDS monitoring is proposed. Experimental results demonstrate that the proposed ASM achieves a more uniform distribution of reconstruction error (i.e., the discrepancy between the original high-frequency signal and its reconstruction from sampled data) and a lower overall error magnitude compared with the traditional fixed-interval sampling method (FISM). This study systematically investigates hydraulic data sampling in WDSs given pressure conditions unaffected by anomalous variations, such as sudden pipe bursts or extraordinary operational actions (e.g., valve maneuvers or atypical pump operations). The analysis is based on signal characteristics and provides a foundation for the design of intelligent monitoring networks in WDSs.