IEEE Engineering Management Society Singapore Chapter
JOINT SEMINAR
on
Experimental Design Considerations for Accelerated Life Testing with Constraints
Speaker(s)
Dr Rong Pan, Arizona State University, USA
Date
31-07-2008
Time
11:00 a.m. to 12:00 p.m.
Venue
Faculty of Engineering, Seminar Room E1-06-08, NUS
Abstract
In this talk we first give a general introduction of the Department of Industrial Engineering at Arizona State University. Dr. Pan’s research interests and his current research effort on Bayesian data analysis for reliability testing will be discussed. Eric Monroe will present his dissertation of optimal experimental designs for accelerated life testing. Accelerated Life Testing is an integral part of reliability evaluations. Often times reliability practitioners are faced with a need to plan tests that involve complexities such as non-linear models and censoring prior to unit failure. This talk will discuss the use of a D-optimality criterion as a tool to improve model parameter estimation in the planning stages of reliability. Parameter estimates will be compared against current industry standard practices for a recent case study from the electronics industry.
Biography
Dr. Rong Pan is Assistant Professor in the Department of Industrial Engineering at Arizona State University. He received his Ph.D. degree in Industrial Engineering from Penn State University in 2002. His research interests include failure time data analysis, design of experiments, multivariate statistical quality control, time-series analysis and control. He is a senior member of ASQ and a member of SRE, IIE, and INFORMS.
Mr Eric M. Monroe holds a Masters degree in Mechanical Engineering from the University of Illinois – Urbana and is a member of the Mechanical Science & Engineering Alumni Board. He is currently a Staff Engineer and Statistician at Intel Corporation with twelve years of experience in the semi-conductor industry. As a Ph.D. student at Arizona State University, his current research interests are in reliability analysis, experimental design, data mining and generalized linear models.
