Real-time Pipe Burst Localization in Water Distribution Networks Using Change Point Detection Algorithms

TL;DR

This paper evaluates change point detection algorithms, specifically CUSUM and Shewhart, for real-time pipe burst localization in water distribution networks, finding CUSUM generally offers better accuracy in simulated scenarios.

Contribution

It introduces a real-time pipe burst localization method using CPD algorithms and compares their effectiveness, highlighting CUSUM's superior performance in simulation.

Findings

CUSUM outperforms Shewhart in localization accuracy

Both algorithms perform well with continuous pressure data

Further testing needed on real-world water networks

Abstract

Change point detection (CPD) has proved to be an effective tool for detecting drifts in data and its use over the years has become more pronounced due to the vast amount of data and IoT devices readily available. This study analyzes the effectiveness of Cumulative Sum (CUSUM) and Shewhart Control Charts for identifying the occurrence of abrupt pressure changes for pipe burst localization in Water Distribution Network (WDN). Change point detection algorithms could be useful for identifying the nodes that register the earliest and most drastic pressure changes with the aim of detecting pipe bursts in real-time. TSNet, a Python package, is employed in order to simulate pipe bursts in a WDN. The pressure readings are served to the pipe burst localization algorithm the moment they are available for real-time pie burst localization. The performance of the pipe burst localization algorithm is evaluated using a key metric such as localization accuracy under different settings to compare its performance when paired with either CUSUM or Shewhart. Results show that the pipe burst localization algorithm has an overall better performance when paired with CUSUM. Although, it does show great accuracy for both CPD algorithms when pressure readings are being continuously made available without a big gap between time steps. The proposed approach however still needs further experiments on different WDNs to assess the performance and accuracy of the algorithm on real-world WDN models.