The rapid advancements of internet of things (IoT) technology and computing power have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for performance improvement in various complex 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. In this talk, two research topics will be discussed in details to elaborate the needs of developing multidisciplinary data fusion and analytics methods for effective online monitoring and prognostic analysis by harnessing the power of Big Data. The first topic introduces a generic data-level fusion methodology, which is capable of integrating multiple sensor signals to effectively visualize and continuously model the evolution of a unit’s health status for degradation modeling and prognostic analysis. This methodology will be tested and validated through a degradation dataset of aircraft gas turbine engines. In the second topic, a dynamic and adaptive sampling algorithm will be introduced to actively decide which data streams should be observed to maximize the anomaly detection capability subject to resources constraint. As a demonstration, we will focus on the real-time detection of the occurrence of solar flares based on a large video stream collected by NASA satellites.
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 is serving as an associate editor of IEEE Transactions on automation science and engineering and IEEE CASE, and is a member of the editorial review board of Journal of Quality Technology. More information can be found in his website: http://kaibo.ie.wisc.edu/index.html.