題目：Contributions to prognosis of remaining useful life (RUL) and predictive maintenance decision-making
主講人：Christophe Brenguer 教授 （Grenoble Institute of Technology，F(xiàn)RANCE）
Antoine Grall 教授 （Univ. de Technologie de Troyes，F(xiàn)RANCE）
時(shí)間：2016年6月15日上午10:00
地點(diǎn)：主樓418
主講人介紹：
Dr. Christophe BERENGUER is a professor in Reliability Engineering, Systems Monitoring and Automatic Control at Grenoble Institute of Technology, France – School of Energy, Water and Environmental Sciences. His research interests are: Reliability engineering and theory, stochastic modelling of system and structure deterioration, performance assessment models of condition-based maintenance policies, reliability models for probabilistic safety assessment (CCF, reliability importance measures, ….) and reliability of safety instrumented systems. Applications to energy, transportation systems. He is an editorial Board of Reliability Engineering and System Safety, and Journal of Risk and Reliability.
Dr. Antoine GRALL is a professor in Maintenance and Reliability Engineering at Univ. de Technologie deTroyes, France. Now he is a Director of the “Statistics, Operations Research and Numerical Simulation” Department (120 people inc. 53 academics) – Member of the board of directors. His research interests are: Stochastic degradation modeling of systems and structures, lifetime prognosis, stochastic modeling for maintenance and reliability, condition-based maintenance policies (performance assessment and optimization, maintenance and on-line monitoring, health monitoring), reliability modeling for probabilistic safety assessment (mainly CCF), reliability of controlled systems.
內(nèi)容介紹：
This talk deals with maintenance decision-making for single-unit deteriorating systems operating under indirect condition monitoring. Based on a degradation and measurement model of crack growth propagation, two new maintenance policies using prognostic information are introduced. Their maintenance cost models are evaluated jointly by analytical and simulation approaches, and are compared with two more classical benchmark models. Such complete models integrating degradation phenomenon, monitoring characteristics, state estimation, prognostics, and maintenance assessment can give rise to fruitful numerical analyses and discussions. The main contributions of the talk are to i) analyze jointly the condition-based and dynamic structure of the considered maintenance policies; ii) propose some effective methods to reduce the effect of measurement uncertainty in condition-based maintenance decision-making; and iii) show the relevance of quantification methods when deciding to resort to prognostic approaches, and to invest in condition monitoring devices.
（承辦：管理科學(xué)與工程系，科研與學(xué)術(shù)交流中心）
