Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
This addresses fault detection for water infrastructure, which is critical for public well-being, but is incremental as it builds on existing anomaly detection methods.
The paper tackles the problem of detecting pipe blockages and background leakages in water distribution networks, proposing an unsupervised online learning framework that outperforms baselines in experiments on two realistic networks.
Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational constraints such as data non-stationarity and limited labeled data. This paper proposes an unsupervised, online learning framework that aims to detect two types of faults in WDNs: pipe blockages, modeled as collective anomalies, and background leakages, modeled as concept drift. Our approach combines a Long Short-Term Memory Variational Autoencoder (LSTM-VAE) with a dual drift detection mechanism, enabling robust detection and adaptation under non-stationary conditions. Its lightweight, memory-efficient design enables real-time, edge-level monitoring. Experiments on two realistic WDNs show that the proposed approach consistently outperforms strong baselines in detecting anomalies and adapting to recurrent drift, demonstrating its effectiveness in unsupervised event detection for dynamic WDN environments.