CV-Prof. Shaoping Wang

Cheung Kong ScholarChair professor of Beihang University，china

Biography：

Pro. Shaoping Wang is “Cheung Kong Scholar” Chair professor of Beihang University and state-council allowance expert. She is also the winner of China Youth Science and Technology Award, winner of Education Ministry New Century Excellent Talents Support Plan, Excellent talents of Ministry of Industry and Information, winner of Excellent Female Medal of Beijing and Beijing Innovative Star. She got PhD degree inmechatronic engineering at Beijing University of Aeronautics and Astronautics in1994.After graduation, she worked as an assistant professor, associate professor and full professor in 1994, 1996 and 2000 respectively. Her research interests include fault diagnosis and health management, reliability and fault tolerant control, fluid power transmission and control, mechatronic control and simulation, design and optimization of mechatronic system, and product life-cycle management. She was the associate chair of Reliability Branch of China Operational Research Society, Fluid Power Transmission and Control Branch of China Aeronautics Society. She is the associate chief editor of Journal of Aeronautics and Beihang University Journal. She published 5 books, around 300 papers include more than 100 SCI indexed papers, 150 EI indexed paper, 32 Granted patents. She gains National Second Prize for innovation, National Second Prize of the Scientific and technological Progress and 16 Ministerial Awards for Scientific and Technological Progress. She also got the national Second Prize on Education and 3 Beijing Prize on Education.

Keynote address:Failure Mechanism and Life Prediction of Fluid Dynamic Seals

Fluid dynamic seals play important role in hydraulic component that influence its reliability and useful life directly. Directing to therelationship between micro interface effect and macro performance degradation of key hydraulic components, the dynamic failure mechanism of seals under mixed lubricating conditions and microscopic surface variation is presented. In order to provide the high preciusion useful life prediction, a life prediction method based on improved particle filtering and failure physical model is given, which can be update by the measured data time to time. Consider the uncertainties associated with operating condition, Markov process is employed to describe the operating condition, and the degradation process is characterized as Markov modulation increment process. The experiment indicate that the proposed degradation model and life predction method is effective especially in the medium and long term life prediction for fluid dynamic seals.