报告题目：Learning Local Cascading Failure Pattern from Massive Network Failure Data
报告时间：2024年6月29日 16:00-17:00
报告地点：综合楼644
报告人：Xun Xiao
报告人简介：
Dr. Xun Xiao is currently a Lecturer in Statistics at the Dept. of Mathematics and Statistics, University of Otago, New Zealand. He received B.Sc. in Statistics from the University of Science and Technology of China in 2011 and Ph.D. degree from the Dept. of Systems Engineering and Engineering Management at City University of Hong Kong in 2016. His current research focuses on industrial statistics and point process modelling. He has published more than 20 papers in peer-reviewed journals, including Technometrics, Journal of Royal Statistical Society: Series C (Applied Statistics), Journal of Quality Technology, IEEE Transactions on Reliability, etc.
报告摘要：
In this talk, I will discuss a novel multivariate point process regression model for a large-scale physically distributed network infrastructure with two failure modes, i.e., primary failures caused by the long-term usage and degradation of the asset, and cascading failures triggered by primary failures in a short period. Large-scale field data on pipe failures from a UK-based water utility are exploited to support the rationale of considering the two failure modes. The two failure modes are not self-revealed in the field data. To make the inference of the large-scale problem possible, a time window for cascading failures is introduced, based on which the likelihood of the pipe failure process can be decomposed into two parts, one for the primary failures and the other for the cascading failure processes modulated by the primary failure processes. The window length for cascading failures is treated as a tuning parameter and it is determined through maximizing the likelihood based on all failure data. To illustrate the effectiveness of the proposed model, two case studies are presented based on real data from the UK-based water utility. Interesting features of the cascading failures are identified from massive field pipe failure data. The results provide insights on more advanced modelling and practical decision-making for both researchers and practitioners.
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