Design of statistically and energy-efficient accelerated life testing experiments

Abstract

The basic idea of Accelerated Life Testing (ALT) is to expose a limited number of test units of a product to harsher-than-normal operating conditions to expedite failures. Based on the failure time data collected in a short time period, an ALT model incorporating the underlying failure time distribution and life–stress relationship can be developed for predicting the reliability of the product under the normal operating condition. However, ALT experiments often consume significant amounts of energy due to the harsher-than-normal operating conditions created and controlled by test equipment. In this article, a new ALT design methodology is developed that has the objective of improving the statistical and energy efficiency of ALT experiments. The resulting statistically and energy-efficient ALT plan depends not only on the reliability of the product to be evaluated, but also on the physical characteristics of the test equipment and its controller. Particularly, the statistical efficiency of each candidate ALT plan needs to be evaluated and the corresponding controller capable of providing the required stress loadings must be designed and simulated to evaluate the total energy consumption of the ALT plan. In this article, mathematical formulations, computational algorithms, and simulation tools are provided to handle such complex experimental design problems. Numerical examples are provided to demonstrate the effectiveness of the proposed methodology in energy reduction in ALT.

Keywords::

• Accelerated life testing
• energy consumption
• optimum experimental design

Additional information

Notes on contributors

Dan Zhang

Dan Zhang received her B.S. degree (2005) and M.S. degree (2008) from Northwestern Polytechnical University, China. In 2011, she received her second M.S. degree in Nuclear Engineering from the University of Tennessee–Knoxville. She received her Ph.D. degree in Systems and Industrial Engineering in May 2014. Her research interests focus on reliability analysis and design of statistically and energy-efficient accelerated tests.

Haitao Liao

Haitao Liao is an Associate Professor of Systems and Industrial Engineering and Director of the Reliability & Intelligent Systems Engineering Laboratory at the University of Arizona, Tucson, Arizona. He received his Ph.D. degree from the Department of Industrial and Systems Engineering at Rutgers University in 2004. He also received M.S. degrees in Industrial Engineering and Statistics from Rutgers University. His research interests focus on modeling of accelerated testing, probabilistic risk assessment, maintenance models and optimization, spare part inventory control, and prognostics. His research has been sponsored by the National Science Foundation, Department of Energy, and U.S. Nuclear Regulatory Commission. He is a member of IIE, INFORMS, and IEEE. He is a recipient of the National Science Foundation CAREER Award in 2010 and the winner of the 2010 & 2013 William A. J. Golomski Award.