Speaker: Kaibo Liu, Department of Industrial and Systems Engineering, UW-Madison
Title: “Big Data Analytics for System Monitoring and Prognostics”
The rapid advancements of sensor technology, communication networks, and computing power have resulted in temporally and spatially dense data-rich environments, which provide unprecedented opportunities for improving operations in complex industrial systems. Meanwhile, it also raises new research challenges on data analysis and decision making, such as heterogeneous data formats, high-dimensional and big data structures, inherent complexity of the target systems, and potential lack of complete a priori knowledge, etc.
In this talk, two topics will be discussed in detail to elaborate the need of developing multidisciplinary data fusion and analytics methods for effective prognostic analysis and system monitoring with Big Data. The first topic is to describe a generic real-time data-level fusion methodology, which is capable of integrating multiple sensor signals to enhance degradation modeling and prognostic analysis. This methodology is tested and validated through a degradation dataset of an aircraft gas turbine engine. In the second topic, a systematic, simple and computationally ef？cient sampling algorithm, which is named as “Top-r based Adaptive Sampling (TRAS)” is proposed for real-time detection of the occurrences of solar flares in a large video stream generated by NASA satellites. If time is allowed, an overview of other research topics for manufacturing and service systems will also be provided.
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.