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首页> 外文期刊>Journal of Water Resources Planning and Management >Evolutionary Algorithm and Expectation Maximization Strategies for Improved Detection of Pipe Bursts and Other Events in Water Distribution Systems
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Evolutionary Algorithm and Expectation Maximization Strategies for Improved Detection of Pipe Bursts and Other Events in Water Distribution Systems

机译：改进的配水系统管道破裂和其他事件检测的进化算法和期望最大化策略

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A fully automated data-driven methodology for the detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level has been recently developed by the authors. This methodology works by simultaneously analyzing the data coming on-line from all the pressure and/or flow sensors deployed in a DMA. It makes synergistic use of several self-learning artificial intelligence (AI) and statistical techniques. These include (1) wavelets for the de-noising of the recorded pressure/flow signals; (2) artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values; (3) statistical process control (SPC) techniques for the short-term and long-term analysis of the burst/other event-induced pressure/flow variations; and (4) Bayesian inference systems (BISs) for inferring the probability that a pipe burst/other event has occurred in the DMA being studied, raising the corresponding detection alarms, and provide information useful for performing event diagnosis. This paper focuses on the (re)calibration of the above detection methodology with the aim of improving the forecasting performances of the ANN models and the classification performances of the BIS used to raise the detection alarms (i.e., DMA-level BIS). This is achieved by using (1) an Evolutionary Algorithm optimization strategy for selecting the best ANN input structures and related parameter values to be used for training the ANN models, and (2) an Expectation Maximization strategy for (re)calibrating the values in the conditional probability tables (CPTs) of the DMA-level BIS. The (re)calibration procedure is tested on a case study involving several DMAs in the U.K. with real-life pipe bursts/other events, engineered pipe burst events (i.e., simulated by opening fire hydrants), and synthetic pipe burst events (i.e., simulated by arbitrarily adding "burst flows" to an actual flow signal). The results obtained illustrate that the new (re)calibration procedure improves the performance of the event detection methodology in terms of increased detection speed and reliability.
机译：作者最近开发了一种全自动的数据驱动方法，用于检测在区域计量区域（DMA）级别引起相似的异常压力/流量变化（例如，未经授权的消耗）的管道破裂和其他事件。该方法通过同时分析来自DMA中部署的所有压力和/或流量传感器的在线数据来工作。它协同使用了几种自学式人工智能（AI）和统计技术。这些包括（1）用于对所记录的压力/流量信号进行消噪的小波； （2）人工神经网络（ANN），用于压力/流量信号值的短期预测； （3）统计过程控制（SPC）技术，用于对突发/其他事件引起的压力/流量变化进行短期和长期分析； （4）贝叶斯推断系统（BIS），用于推断正在研究的DMA中发生管道爆裂/其他事件的可能性，并发出相应的检测警报，并提供可用于进行事件诊断的信息。本文着重于对上述检测方法的（重新）校准，以期改善ANN模型的预测性能以及用于发出检测警报的BIS（即DMA级BIS）的分类性能。这是通过以下方法实现的：（1）一种进化算法优化策略，用于选择最佳的ANN输入结构和相关参数值以用于训练ANN模型;（2）一种期望最大化策略，用于（重新）校准ANN模型中的值DMA级BIS的条件概率表（CPT）。 （重新）校准程序是在涉及英国多个DMA的案例研究中进行测试的，包括实际的管道爆裂/其他事件，工程管道爆裂事件（即，通过打开消火栓进行模拟）和合成管道爆裂事件（即，通过将“突发流量”任意添加到实际流量信号进行模拟）。获得的结果说明，新的（重新）校准过程在提高检测速度和可靠性方面提高了事件检测方法的性能。

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