230
Views
10
CrossRef citations to date
0
Altmetric
Original Articles

Modelling accelerated life testing based on mean residual life

&
Pages 689-696 | Received 04 Oct 2002, Accepted 01 Jul 2003, Published online: 23 Feb 2007
 

Abstract

Accelerated life testing (ALT) is a widely used approach for reliability demonstration and prediction. Extensive research on ALT models has been focused on Accelerated Failure Time (AFT) models, Proportional Hazards (PH) models and some extensions of these two models. Mean residual life function provides a more descriptive measure of the aging process than the hazard function; however, it has not been used in modelling ALT. In this paper, we propose an ALT model based on a Proportional Mean Residual Life (PMRL) model and demonstrate its applicability in the reliability field. The model utilizes accelerated conditions data to estimate the reliability measures under normal operating conditions and provides a viable alternative to the accelerated failure time (AFT) model and the proportional hazards (PH) model. Some results concerning aging properties for the PMRL model are also studied.

Acknowledgments

Dr. Wenbiao Zhao is a Research Scientist in ReliaSoft Corporation; he received his Ph.D in Industrial and Systems Engineering from Rutgers, the State University of New Jersey in 2003, his dissertation focuses on Accelerated Life Testing Modeling, Planning and Optimization. He also received his M.S. (1994), PhD (1997) in Automatic Control (Tsinghua University, Shanghai Jiao Tong University) in China, and M.S. in Statistics (2002) and M.S. (2002) in Industrial and Systems Engineering from Rutgers University. He is a co-recipient of the 2005 Golomski Award for the outstanding paper in the Annual Reliability and Maintainability Conference. He has published many papers in H-infinity Control, Structure Singular Value Theory, and Reliability Engineering. His current research interests include Reliability Prediction, Accelerated Life/Degradation Testing, Data Analysis, and Robust Optimization. He is a member of IEEE, INFORMS.

Dr. E. A. Elsayed is a Professor in the Department of Industrial Engineering, Rutgers, the State University of New Jersey. He is also Director of the NSF / Industry / University Co-operative Research Center for Quality and Reliability Engineering, Rutgers-Arizona State University. His research interests are in the areas of quality and reliability engineering and Production Planning and Control. He is a co-author of Quality Engineering in Production Systems, McGraw Hill Book Company, 1989. He is the author of Reliability Engineering, Addison-Wesley, 1996. These two books received the 1990 and 1997 IIE Joint Publishers Book-of-the-Year Award respectively. He is a co-recipient of the 2005 Golomski Award for the outstanding paper in the Annual Reliability and Maintainability Conference. Dr. Elsayed is also a co-author of Analysis and Control of Production Systems, Prentice-Hall, 2nd Edition, 1994. He is the author and co-author of work published in the IIE Transactions, IEEE Transactions, and the International Journal of Production Research. His research has been funded by the DoD, FAA, NSF and industry. Dr. Elsayed has been a consultant for AT&T Bell Laboratories, Ingersoll-Rand, Johnson & Johnson, Personal Products, AT&T Communications, Ethicon and other companies. Dr. Elsayed was the Editor-in-Chief of the IIE Transactions and the Editor of the IIE Transactions on Quality and Reliability Engineering. He is also an Editor for the International Journal of Reliability, Quality and Safety Engineering. He serves on the editorial.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,413.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.