Data Fusion for Quality Improvements in Complex Systems

Data Fusion for Quality Improvements
in Complex SystemsJionghua (Judy) JinAssistant Professor
Department of Systems and Industrial Engineering
University of ArizonaABSTRACTThe rapid advancement of sensing and computing techniques provides unprecedented opportunities for quality improvement in both manufacturing and service industries. The wide deployment and applications of automatic sensing devices and computer systems have resulted in both temporally and spatially dense data-rich environments, which bring new challenges in data acquisition, processing, simulation, information extraction, decision making and dissemination. The development of novel quality engineering methodologies is highly demanded and has been emerging in various technical directions through objected oriented data fusion which includes data preprocessing and cleaning from massive and different domain data, feature extraction from multivariate/multi-attribute data for information extraction and representation, data structure modeling, pattern analysis, physical inference, knowledge discovery, and real time decision making for system change detection, root cause diagnostics, and control with well-informed risk analysis and managements . Data fusion, through integration of engineering domain knowledge with data analysis techniques from advanced statistics, signal processing, decision making and control, represents one of the frontiers in quality improvement research for complex systems. This presentation will provide an overview of ongoing data fusion research activities and its applications in both manufacturing industry and service industry. The basic concepts in data fusion research will be discussed with emphasis on promoting the integration of disparate information into a cohesive entity to make effective decision for variation reduction and quality improvements. Examples of methodological developments and their applications will be discussed to demonstrate the characteristics of data fusion research and the need of multidisciplinary efforts. Detailed discussions will be given on DOE-based APC methodology development for variation reduction beyond the robust design to fully utilize online observable noise variable information with the consideration of data uncertainty.WEDNESDAY, FEBRUARY 16, 2005
GERONTOLOGY BUILDING (GER) ROOM 309
3:30 – 4:30 PMBio for Dr. Judy JinJionghua (Judy) Jin is an assistant professor in the Department of Systems and Industrial Engineering at the University of Arizona. She received her B.S. and M.S. degree in Mechanical Engineering, both from Southeast University in 1984 and 1987 respectively, and her Ph.D. degree in Industrial and Operations Engineering at the University of Michigan in 1999. Dr. Jin's research interests focus on data fusion and decision making for complex systems to develop new methodologies for system modeling, condition monitoring and fault diagnosis, process control, knowledge discovery and decision making. Her research emphasizes multidisciplinary approach by integrating applied statistics, signal processing, reliability engineering, system control, and decision-making theory. Over the years, she has been working on various research programs including multistage manufacturing processes of semiconductor manufacturing, assembly, stamping, service industry of transportation and telecommunication. Her research has being sponsored by National Science Foundation, U.S. Air Force Office of Scientific Research, SME Education Foundation, Department of Transportation . Bureau of Transportation Statistics, Federal Highway Administration/Arizona Department of Transportation, Arizona State Foundation, Department of Energy, and various industrial support funds/collaborations from General Motors, Caterpillar Inc., Daimler Chrysler Corp, and Global Solar Energy Inc. etc.She is the chair-elect of the Quality, Statistics, and Reliability Section of INFORMS, and also an elected board member for the Quality Control & Reliability Engineering Division of IIE. She plays various editorial roles including a guest editor for two special issues for The International Journal of Flexible Manufacturing Systems, a member on the Editorial Board of IIE Transactions on Quality and Reliability. She has received a number of awards including the NSF-CAREER Award in 2002; Excellence at the Student Interface Award from University of Arizona in 2001; Best Paper Award from ASME in 2000, Best Paper Award from IIE Transactions in 2005, and Presidential Early Career Award for Scientists and Engineers (PECASE) in 2004. Dr. Jin is a member of INFORMS, IIE, ASQC, ASME, and SME.More information about Dr. Judy Jin can be found at http://tucson.sie.arizona.edu/ faculty/jhjin/.

Location: Ethel Percy Andrus Gerontology Center (GER) - 309

Audiences: Everyone Is Invited

Contact: Shah Nirav