报告题目：Learning Short and Long Term Failure Patterns from Massive Network Failure Data
报 告 人：叶志盛 副教授 新加坡国立大学
报告时间：2024 年05月29日 上午10:00-11:00
报告地点：#腾讯会议：949-548-509
或点击链接直接加入会议：https://meeting.tencent.com/dm/G8DnYaXps5F2
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
Many lifeline infrastructure systems consist of thousands of components configured in a complex directed network. Disruption of the infrastructure constitutes 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. In this talk, we focus on both short term cascading failures and long term ageing failures. Repair of a pipe might generate shocks to neighbouring pipes and cause short term cascading failures. Understanding the short-term cascading failure is important for the utility to allocate additional resources to monitor the neighbouring pipes after a repair. On the other hand, understanding long-term failures is helpful in risk analysis of the whole pipe network and prioritizing replacements of old pipes. Statistical modelling of the two failure modes are extremely challenging because of the large pipe network and the huge failure data set. We develop novel statistical methods that are computationally tractable to fit the data. Applying the methods to a large data set from the Scottish Water network, we demonstrate the usefulness of our models 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立大学工业与系统工程系。现在为新加坡国立大学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。
