报告内容摘要

Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion has provided an essential tool for service decision making. This talk will provide an overview of the recent advancement regarding this topic, with a particular focus on the generic data-driven approaches to constructing an effective health index that combines multiple and heterogeneous sensor data to better characterize the health condition of units. The health index can then be used to support smart service decisions, which will lead to: (i) closer monitoring of a unit’s health status; (ii) quicker fault diagnosis; (iii) more accurate forecast of a unit’s remaining lifetime; and (iv) proactive maintenance and control decisions better aligned to future conditions and performance. The proposed methods are tested and validated through the degradation datasets of aircraft gas turbine engines and other complex systems.
 
报告人简介

Dr. Kaibo Liu is an assistant professor at the department of Industrial and Systems Engineering, University of Wisconsin-Madison. He received the B.S. degree in industrial engineering and engineering management from the Hong Kong University of Science and Technology, Hong Kong, China, the M.S. degree in statistics and the Ph.D. degree in industrial engineering from the Georgia Institute of Technology, Atlanta, respectively. Dr. Kaibo Liu’s research is in the area of system informatics and data analytics, with an emphasis on the data fusion approach for system modeling, monitoring, diagnosis and prognostics. The significance of his research has been evidenced by the wide recognition in a broad of research communities in Quality, Statistics, Reliability and Data Mining, including several best paper awards from INFORMS and ISERC. In addition, his research results and papers have led to successful proposals jointly funded by NSF and DOE. He was also the winner of the Gilbreth Memorial Fellowship from Institute of Industrial Engineers (IIE) in 2012, the winner of the Richard A. Freund International Scholarship from American Society for Quality (ASQ) in 2013, and the winner (2nd place) of the Pritsker Doctoral Dissertation Award from IIE in 2014.
 
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