Speaker：ZHANG YI, PHD，Department of industrial engineering, Peking University

    Time： 14:00-15:00 PM, April 6th

    Place：F207,School of Mechanical Engineering

    Host：GENG Na, Associate Professor

   
     Abstract

    Due to the rapid development of sensing and computing technologies, sensing signals are now widely used in advanced manufacturing and operation systems for process monitoring, diagnosis/prognosis, and optimization. In this talk, we focus on modeling the process by using the collected sensing signals and integrating the dynamics of the process. Specifically, two examples including the progressive stamping process and pipe connection process, are discussed. In progressive stamping processes, we propose a novel automatic process monitoring method using the recurrence plot (RP), and the recurrence quantification analysis (RQA) is adopted to characterize the critical patterns for condition identification; in pipe connection processes, we establish a two-phase state space model by incorporating elastic mechanics caused by local mechanical deformation, and a recursive particle filter algorithm is developed to estimate critical point locations with consideration of the unequal length of signals and horizontal oscillations caused by mechanical return difference. Simulation and real case studies are performed, and results show that the proposed method achieves satisfactory performance for condition monitoring.

    
    Biography
    Dr. Xi Zhang is an associate professor at the department of Industrial Engineering and Management, Peking University. He received the B.S. degree in mechanical engineering from Shanghai Jiaotong University, China, 2006, and the Ph.D. degree in industrial engineering from the University of South Florida, Tampa, 2009, respectively. His research interests focus on the field of engineering data analytics, with an emphasis on integration of data analysis approach and engineering physics for process monitoring, diagnosis/prognostics, and optimization, which has been widely implemented in the area of advanced manufacturing and healthcare delivery. His research has been evidenced by high quality journal paper and best paper award in a broad of research communities including INFORMS QSR Section and Robotics, IIE and Automation Society. His research has been funded by NSFC, MOST, MOE, etc. He is the member of INFORMS, IIE, IEEE and ASQ.