Abstract

Online health monitoring and fault diagnosis in complex systems is essential for reducing the troubleshooting time and maintenance costs and thus improving system availability. To meet the demand for dynamic and highly reliable real-time fault diagnosis for complex systems, we extend the dynamic uncertain causality graph (DUCG) by proposing novel temporal causality modeling and reasoning methods. A new methodology, the Cubic DUCG, is therefore developed. It exploits an efficient scheme for compactly representing and accurately reasoning about the dynamic causalities in the system fault spreading process. This talk will discuss how DUCG is applied in fault diagnosis and PSA.

Speaker

Professor Qin Zhang China

Professor of Institute of Nuclear and New Energy Technology and Department of Computer Science and Technology

Tsinghua University

Qin Zhang is a member of the Standing Committee of the 13th Chinese People’s Political Consultative Conference, an emeritus member of the China Association for Science and Technology, a member of the International Nuclear Energy Academy, head of the strategical advisory expert team for China National Key Project of Nuclear Power, a fellow of China Association for Artificial Intelligence (CAAI) and director of the specialized committee for uncertainty in AI of CAAI, director of the academic advisory committee of China Intelligent Property Society, professor of Institute of Nuclear and New Energy Technology and Department of Computer Science and Technology, Tsinghua University.