报告内容摘要：

Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used 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 methodologies provide an essential tool for degradation modeling and prognostics. In the first part of this talk, we propose a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. In the second part of this talk, we further develop a convex quadratic formulation that combines the information from the degradation profiles of historical units and the in-situ sensory data from an operating unit to online estimate the failure threshold of this particular unit in the field. With a more accurate estimation of the failure threshold of the operating unit in real time, a better remaining useful life prediction is expected. Simulation studies and a case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the proposed methods and further compare with existing literature.
 
 
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, prognostics and maintenance. 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 and several featured articles from IIE and INFORMS magazines. In addition, his research results and papers have led to successful funding supports by NSF, DoD, and Industry. 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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