Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows
Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows
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DOI：
10.1061/(asce)wr.1943-5452.0000030
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发表时间：
2010-05-01
影响因子：
3.1
通讯作者：
Machell, J.
中科院分区：
文献类型：
--
作者：
Mounce, S. R.;Boxall, J. B.;Machell, J.
从供水管网渗漏而损失的水通常是可观的。随着水资源压力的增加，供水服务提供者越来越重视尽量减少这种损失。本文提出的工作目的是评估在线应用人工智能系统在地区米面积（DMA）级别检测泄漏/爆裂的效果。使用不断更新的历史数据库来训练混合密度人工神经网络模型，该数据库构建了未来流动剖面的概率密度模型。采用模糊推理系统进行分类；它将最近观测到的流量值与预测流量在时间窗口内进行比较，以便在发生异常流量情况时发出警报。从预测流量的概率密度函数中，模糊推理系统提供了与每个检测相关联的置信区间，这些置信区间为警报的过滤和排序提供了有用的信息。此外，对异常流量的准确估计可以进一步帮助对警报进行排序。在一个案例研究中，英国的供水系统使用了通用分组无线电服务提供的接近实时的流量数据。在线突发警报系统与现有的平线警报系统一起运行，每小时连续分析144个dma。新系统确定了一些事件，并在控制室检测到它们之前发出警报；要么通过平面报警，要么通过客户联系。在两个月的试验期内，给出了与爆裂报告和随后的管道维修的警报相关性的例子。44%的警报被发现与维修数据或客户联系确认的突发事件相对应，32%的警报被确认为人工分析中不寻常的短期需求，9%与已知的工业事件有关，只有15%是幽灵。结果表明，该系统是一种有效可行的给水系统突发在线检测工具，具有节约用水和改善客户服务的潜力。
Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for filtering and ranking alerts. Additionally an accurate estimate of abnormal flow magnitude is produced to further aid in ranking of alerts. A water supply system in the U.K. was used for a case study with near real-time flow data provided by general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system, and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events and alerts were raised prior to their detection in the control room; either through flat-line alarms or customer contacts. Examples are given of alert correlation with burst reports and subsequent mains repairs for a 2-month trial period. Forty four percent of alerts were found to correspond to bursts confirmed by repair data or customer contacts, 32% of alerts were confirmed as unusual short-term demand from manual analysis, 9% were related to known industrial events, and only 15% were ghosts. The results indicate that the system is an effective and viable tool for online burst detection in water distribution systems with the potential to save water and improve customer service.