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【 Ming Lecture,2023,lssue 5 】2-23 Professor Zhai Qingqing, School of Management, Shanghai University: Multivariate Stochastic Degrad

Time: February 23, 2023 (Thursday) 11:00am - 12:30am

Location: 317 Main Building

Speaker: Associate Professor Zhai Qingqing, School of Management, Shanghai University

About the speaker: Zhai Qingqing, Associate Professor, School of Management, Shanghai University, Shanghai Young Oriental Scholar, is the Deputy Secretary General of Reliability Engineering Branch of China Field Statistics Research Society, and the director of Industrial Engineering Branch of China Association of Superior Selection Method and Economic Mathematics. He received his doctorate degree in Systems Engineering from Beihang University in 2015. From 2015 to 2017, he was a research fellow at the Department of Industrial Systems Engineering and Management, National University of Singapore. His main research interests include statistical models of degradation, reliability modeling, and game theory. He has published more than 40 papers in Technometrics, IISE Transactions, ITII, EJOR, ITR, RESS and other international journals.

Brief introduction of the report:

For industrial products, the common degradation of multiple performance indicators is often encountered. Modeling and analysis of multi-performance dependent degradation is of great practical significance. In practice, the dependencies between multiple performance degradations may result either from the degradation of the health state of the product itself or from the external environment in which the product is used. In this paper, a multivariate Wiener process model is proposed to solve this problem. Among them, we introduce a common random time scale for multiple degradation indicators to explain the environmental effects. At the same time, multiple degradation indicators are assumed to contain a common Wiener process random component to explain the dependence caused by the degradation of the overall health state of the product. Aiming at this model, we study the parameter estimation method based on EM algorithm, and propose the reliability calculation method based on bridge sampling algorithm. The practical effect of the proposed method is proved by the application and comparative study on the degradation data of a certain coating.

(Organized by: Department of Management Science and Logistics, Research and Academic Exchange Center)

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