Time: 9:30 am, June 29, 2018 (Friday)
Location: Main Building 418
Introducition：
Reliability demonstration test (RDT) has been widely used in engineering design to verify if a product has met a certain reliability requirement. Such tests are usually conducted and analyzed based on binomial theory for the number of failures or analysis of failure times. Unlike these traditional methods, a degradation-based RDT method is proposed in this paper. Appropriate implementation of this method will speed up reliability demonstration, especially for highly reliable products. However, a big challenge is the measurement error that cannot be avoided in degradation data collection. To incorporate the impact of measurement errors in degradation-based RDT, a random effects stochastic process model explaining both the evolution of product degradation and measurement error is proposed. Under this model, a statistical inference method based an expectation-maximization algorithm is developed to estimate the model parameters. Moreover, the optimal design of degradation-based RDT is developed to minimize the total testing cost considering both the producer’s and consumer’s risks. A numerical example is presented to illustrate the use of the proposed RDT method in practice.
Personal Profile：
Dr. Haitao Liao is a Professor and Hefley Endowed Chair in Logistics and Entrepreneurship in the Department of Industrial Engineering at University of Arkansas – Fayetteville. He received a Ph.D. degree in Industrial and Systems Engineering from Rutgers University in 2004. He also earned M.S. degrees in Industrial Engineering and Statistics from Rutgers University, and a B.S. degree in Electrical Engineering from Beijing Institute of Technology. His research interests include reliability models, maintenance and service logistics, prognostics, probabilistic risk assessment, and data analytics. He has authored over 60 refereed journal publications. He currently serves as Associate Editor for the Journal of Quality Technology and IISE Transactions on Quality and Reliability Engineering. He was the Chair of INFORMS Quality, Statistics and Reliability (QSR) Section, and the President of IIE Quality Control and Reliability Engineering Division. He received a National Science Foundation CAREER Award in 2010, William A.J. Golomski Best Paper Awards in 2010, 2013 and 2017, 2015 Stan Ofsthun Best Paper Award, 2017 Alan O. Plait Award for Tutorial Excellence, among others.
