【Title】Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis 

【Presenter】Hui Yang, Assistant Professor, Industrial and Management Systems Engineering, University of South Florida 

【Host】 Dr. Kaibo Wang 

【Date】Monday May 25, 2015  10:30-11:30 

【Venue】Room 510, Shunde Building 

【Abstract】Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. Effective monitoring and control of nonlinear dynamics will increase system quality and integrity, thereby leading to significant economic and societal impacts. In order to cope with system complexity and increase information visibility, modern industries are investing in a variety of sensor networks and dedicated data centers.  Real-time sensing gives rise to “big data”. Realizing the full potential of “big data” for advanced quality control requires fundamentally new methodologies to harness and exploit complexity. This talk will present novel nonlinear methodologies that mine dynamic recurrences from in-process big data for real-time system informatics, monitoring, and control. Recurrence (i.e., approximate repetitions of a certain event) is one of the most common phenomena in natural and engineering systems. For examples, the human heart is near-periodically beating to maintain vital living organs. Stamping machines are cyclically forming sheet metals during production. Process monitoring of dynamic transitions in complex systems (e.g., disease conditions or manufacturing quality) is more concerned about aperiodic recurrences and heterogeneous recurrence variations. However, little has been done to investigate heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. This talk will present the state of art in nonlinear recurrence analysis and a new heterogeneous recurrence methodology for monitoring and control of nonlinear stochastic processes. Specifically, the developed methodologies will be demonstrated in both manufacturing and healthcare applications. The proposed methodology is generally applicable to a variety of complex systems exhibiting nonlinear dynamics, e.g., precision machining, sleep apnea, aging study, nanomanufacturing, biomanufacturing. In the end, future research directions will be discussed.

【Bio】Dr. Hui Yang is an Assistant Professor in the Department of Industrial and Management Systems Engineering at the University of South Florida. His research focuses on sensor-based modeling and analysis of complex systems for process monitoring/control, system diagnostics/prognostics, quality improvement, and performance optimization. His research received several best paper awards such as the IBM Best Paper Award in 2011 IEEE International Conference of Engineering in Medicine and Biology Society, and ISERC Conference Best Paper Awards (Manufacturing and Design Track in 2009, Computer and Information Systems Track in 2010, 2014 and 2015). Dr. Yang is a recipient of NSF CAREER Award (2015). Currently, he serves as the chair-elect of INFORMS quality, statistics and reliability society, an associate editor of Computers and Industrial Engineering, and a guest editor for IEEE Intelligent Systems, Information Systems and e-Business Management (Springer). He is a professional member of IEEE, IIE, AHA, ASEE and INFORMS.