The rapid advancements of internet of things (IoT) technology and cyber-physical infrastructure 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. Various other examples will be illustrated as well by using the proposed methods if time allows.
September 12 @ 12:30
12:30 pm (1h)
Discovery Building, Orchard View Room