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首页> 外文期刊>Urban water journal >Extending the Yule process to model recurrent pipe failures in water supply networks
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Extending the Yule process to model recurrent pipe failures in water supply networks

机译:扩展Yule过程以对供水网络中的经常性管道故障进行建模

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This paper shows how to construct, within the mathematical framework of counting process theory, a Linear Extension of the Yule Process (LEYP) to model the recurrent failures of pressure pipes in water supply networks. The choice of the counting process framework is motivated by an analysis of the advantages and shortcomings of the modelling approaches proposed in the literature over the last thirty years. The parametric nature of the LEYP model enables the prediction of future failures, accounting for the effect of previous failures, pipe ageing, and explanatory factors, assuming proportional hazards. The counting process is Markovian by definition and shown to follow a negative binomial distribution. This property leads to a useful and simple formula for computing conditional predictions given past observed failures. The likelihood of the parameters with respect to a sequence of observed failures is derived using product-integration. Maximum likelihood estimates of the model parameters can be calculated with left-truncated data, i.e. failure observations restricted to a time interval that possibly starts long after pipe commissioning. Procedures for testing the significance of the model parameters, for assessing the model goodness of fit, and for validating the model predictions are presented. The predictive performance of the LEYP model is finally illustrated with extensive failure data provided by a French water utility.
机译:本文展示了如何在计数过程理论的数学框架内构造尤尔过程的线性扩展(LEYP),以对供水网络中压力管道的反复失效进行建模。计数过程框架的选择是通过对过去30年文献中提出的建模方法的优缺点进行分析得出的。 LEYP模型的参数性质使您能够预测未来的故障,并考虑到先前的故障,管道老化和解释性因素(假设成比例的危害)的影响。根据定义,计数过程是马尔可夫模型,并且显示为负二项分布。对于过去观察到的故障,此属性可得出一个有用且简单的公式,用于计算条件预测。相对于一系列观察到的故障,参数的可能性是通过乘积积分得出的。可以使用左截断的数据来计算模型参数的最大似然估计值,即,故障观察仅限于在管道调试后很长时间开始的时间间隔。提出了测试模型参数的重要性,评估模型拟合优度和验证模型预测的程序。最后,由法国自来水公司提供的大量故障数据说明了LEYP模型的预测性能。

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