无需标注数据，实时检测供水管网堵塞与渗漏。
Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
- 用LSTM-VAE结合双漂移检测，识别集体异常和概念漂移。
- 在两个真实管网中均优于基线模型，适应动态变化环境。
- 轻量设计适合边缘设备，支持实时监测。
供水管网（WDNs）关乎公共福祉与经济稳定，面临管道堵塞与背景渗漏等挑战，且受数据非平稳性与标注数据匮乏制约。本文提出一种无监督在线学习框架，用于检测两类故障：将管道堵塞建模为集体异常，将背景渗漏建模为概念漂移。方法融合长短期记忆变分自编码器（LSTM-VAE）与双漂移检测机制，在非平稳条件下实现鲁棒检测与自适应。其轻量化、低内存设计支持边缘端实时监控。在两个真实供水管网上的实验表明，该方法在异常检测与周期性漂移适应方面持续优于强基线模型，验证了其在动态供水环境中的无监督事件检测有效性。
原文摘要 · Abstract (English)
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.
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