报告题目：Data-Driven Risk Evaluation of a Large-Scale Pipe Network

报 告 人： 叶志盛 副教授 新加坡国立大学

报告时间：2022 年 05 月 13 日 下午 16:00-17:00

报告地点：腾讯会议 ID：122-570-145

或点击链接直接加入会议：https://meeting.tencent.com/dm/wyPxjjdNru74

校内联系人：赵世舜 zhaoss@jlu.edu.cn

报告摘要：A lifeline infrastructure system usually has thousands of elements configured in a complex network structure. The failures/repairs of the infrastructure constitute a recurrent failure process over a directed network. Statistical inference for such network recurrence data is challenging because of the large number of nodes with irregular connections among them. Based on 16-years of operation records in Scottish Water, we propose a network Gamma-Poisson Autoregressive NHPP (GPAN) model for recurrent failure data from a large-scale directed physical network. The model consists of two layers, where the temporal layer applies an NHPP with frailty for each node, and the spatial layer uses a well-orchestrated gamma-Poisson autoregressive scheme to establish correlations among the frailties. Under the network-GPAN model, we develop a sum-product algorithm to compute the marginal distribution for each frailty conditional on the recurrence data. The ability to rapidly compute these marginal distributions allows adoption of an EM algorithm for estimation. The developed model is applied to a subset of the Scottish Water network where we demonstrate the usefulness in aiding operation management and risk assessment of the water utility.

报告人简介：叶志盛博士本科毕业于清华大学材料科学与工程系，博士就读于新加坡国大工业与系统工程系。现在为新加坡国大工业系统工程与管理系副教授。他的主要研究方向包括剩余寿命预测，可靠性建模，及数据驱动的运营决策。