報告題目：Learning Short and Long Term Failure Patterns from Massive Network Failure Data
報 告 人：葉志盛 副教授 新加坡國立大學
報告時間：2024 年05月29日 上午10:00-11:00
報告地點：#騰訊會議：949-548-509
或點擊鍊接直接加入會議：https://meeting.tencent.com/dm/G8DnYaXps5F2
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
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.
報告人簡介：
葉志盛博士，本科畢業于清華大學材料科學與工程系，博士就讀于新加坡國立大學工業與系統工程系。現在為新加坡國立大學工業系統工程與管理系副教授。他的主要研究方向包括剩餘壽命預測，可靠性建模，及數據驅動的運營決策。
