题目：Artificial Intelligence for Reliability
时间：2026年3月25日 10:00-11:30
地点：德赢vwin体育官网登录 振华会议室
邀请人：陈震 副教授（工业工程与管理系）
Biography
Enrico Zio, received the MSc degree in nuclear engineering from Politecnico di Milano in 1991 and in mechanical engineering from UCLA in 1995, and the Ph.D. degree in nuclear engineering from Politecnico di Milano and in probabilistic risk assessment at MIT in 1996 and 1998, respectively. He is currently full professor at the Centre for research on Risk and Crises (CRC) of Ecole de Mines, ParisTech, PSL University, France, full professor and President of the Alumni Association at Politecnico di Milano, Italy. He is member and vice-president of the Board of Directors of Fondazione Politecnico di Milano, and IEEE and Sigma Xi Distinguished Lecturer. In 2020, he has been awarded the prestigious Humboldt Research Award from the Alexander von Humboldt Foundation in Germany. His research focuses on the modelling of the failure-repair-maintenance behaviour of components and complex systems, for the analysis of their reliability, maintainability, prognostics, safety, vulnerability, resilience and security characteristics, and on the development and use of Monte Carlo simulation methods, artificial intelligence techniques and optimization heuristics. He is author and co-author of more than 10 books and more than 600 papers on international journals, Chairman and Co-Chairman of several international Conferences, associate editor of several international journals and referee of more than 20. He has an h-index of 115 on Google Scholar with over 50,000 citations.
Abstract
The advancement of monitoring capabilities and data/image/text processing technologies have enabled the abundant collection of knowledge, information and data (KID) on equipment operation and their analytic processing to estimate and predict components and systems states. In particular, the processed KID can be used to estimate and predict the state of health (SoH) of components and systems, to characterize their “living reliability” and take properly informed decisions of operation and maintenance.
In this lecture, we look at the grown ability of analysing KID by artificial intelligence (AI) algorithms to mine out information relevant to the assessment and prediction of the functional state of equipment, and show how to use these capabilities in support to reliability assessment and intelligent maintenance. Also, an important critical reflection is offered with regards to the challenges that need to be addressed for the actual deployment of AI in industrial practice.
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