[Mingli Lecture 2021 Issue 29]

Time: 10:30-12:00 am, May 21 (Saturday)

Tencent Meeting Number: 911 987 113

Speaker: Professor Peng Rui, Beijing University of Technology

Speaker Profile:

  Peng Rui: Ph.D., School of Economics and Management, Beijing University of Technology Employed professor, doctoral supervisor. In July 2007, he graduated from the Junior Class of the University of Science and Technology of China with a bachelor's degree; in October 2011, he graduated from the National University of Singapore with a doctoral degree. In January 2012, he entered the Dongling School of Economics and Management, University of Science and Technology Beijing. In July 2014, he was promoted to associate professor. In November 2018, he joined the School of Economics and Management, Beijing University of Technology. In January 2020, he was named a doctoral supervisor. His main research areas are energy system risk assessment and policy optimization. He has published more than 100 SCI journal papers, 5 monographs, presided over or participated in 8 National Natural Science Foundation of China or national key R&D programs, and presided over the Beijing Science and Technology Rising Star Project. Editorial board member of Reliability Engineering & System Safety, IEEE senior member, Deputy Secretary General of Reliability Branch of China Field Statistics Society, Executive Director of Industrial Engineering Branch of China Optimal Method, Coordination Method and Economic Mathematics Research Society, Executive Director of Reliability Branch of China Operations Research Society, China Commander Executive Director of the Reliability Special Committee of the Society for Control and Control. Elsevier China Highly Cited Scholars for four consecutive years from 2018 to 2021.

Introduction to the report:

  Numerous power assets, such as power cables and refrigerator, have inevitably confronted with severe energy consumption problems. An essential topic in maintenance engineering of such assets is to balance energy consumption mitigation and malfunction risk control, such that to reduce production/delivery losses. This paper established a novel proactive maintenance framework, which arranges two dimensions of control limits to control maintenance frequencies: (a) operational age and (b) consumption severity to guide repair and spare part replacement. The energy consumption process is structured by a Semi-Markov chain. The asset is preventively replaced upon a certain operational age, or when the unit time energy consumption reaches a pre-specified threshold, given that the time to scheduled age-based replacement is greater than a threshold. The maintenance optimization model is formulated, followed by an in-depth exploration of its analytical structures. The superior performance of the proposed framework over conventional maintenance policies is demonstrated by a numerical experiment on high-voltage power cables.

(Organizer: Department of Management Science and Logistics, Scientific Research and Academic Exchange Center)