Abstract: The rapid advancements in Internet of Things (IoT) technology and cyber-physical infrastructure have resulted in a temporally and spatially dense data-rich environment that 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 detail to elaborate the need for 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 that 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.
Bio: Kaibo Liu is an assistant professor in the industrial and systems engineering department at the 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 and the M.S. degree in statistics and the Ph.D. degree in industrial engineering from the Georgia Institute of Technology. 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 service decision making.