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

Release time:June 29, 2017 / Siying He

Topic:Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis    

Speaker:    

Hui Yang
Harold and Inge Marcus Career Associate Professor
The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
The Pennsylvania State University, University Park    

Date:June 30, 14:00-15:00    

Venue:Room 502, Weimin 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, Nano manufacturing, bio manufacturing. In the end, future research directions will be discussed.    

 

Bio of the Speaker:    

Dr. Hui Yang is the Harold and Inge Marcus Career Associate Professor inthe Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA.  Prior to joining Penn State in 2015, he was an Assistant Professor in the Department of Industrial and Management Systems Engineering at the University of South Florida from 2009 to 2015.  He is a recipient of 2015 Outstanding Faculty Award at the University of South Florida.    

Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization. His research program is supported by National Science Foundation (including the prestigious NSF CAREER award) and 2 equipment grants from NSF and State of Florida for laboratory computing infrastructure improvement.    

Dr. Yang is the president (2015-2016) of INFORMS Quality, Statistics and Reliability (QSR) society and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also an associate editor for IEEE Journal of Biomedical and Health Informatics (JBHI), and an Associate Editor for the Proceedings of 2017 IEEE International Conference on Automation Science and Engineering. He serves as a referee for a diverse set of top tier research journals such as Nature, Physical Review, IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics, Biophysical Journal, IIE Transactions, Technometrics, and IEEE Transactions on Automation Science and Engineering.  He is a professional member of IEEE, IEEE EMBS, INFORMS, IIE, ASEE and American Heart Association (AHA).    

 

School of Reliability and Systems Engineering