DESIGN OF OPTIMUM SIMPLE STEP-STRESS ACCELERATED LIFE TESTING PLANS

Elsayed A. Elsayed and Hao Zhang
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Elsayed A. Elsayed: Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ 08854-8018, USA
Hao Zhang: Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ 08854-8018, USA

Chapter 2 in Recent Advances in Stochastic Operations Research, 2007, pp 23-38 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThe mission time of today's products is extended so much that it is difficult to observe failures under normal operating conditions. Therefore, accelerated life testing (ALT) is widely conducted to obtain failure time data in a much shorter time and to make inference about reliability at normal conditions. The accuracy of the reliability prediction is dependent on well-designed ALT plans. A step-stress ALT allows the test conditions to change at a given time or upon the occurrence of a specified number of failures. Stepstress accelerated Life testing, an important type of ALT, is more difficult to model compared with constant stress ALT, however it yields failures more quickly. A test unit starts at a specified low stress. If the unit does not fail in a specified time, the stress is increased and held constant for another specified time. Stress is then repeatedly increased and held constant until the test unit fails. In this paper, we propose a procedure to determine the parameters of the optimum simple step-stress testing plan so that the reliability prediction at normal conditions is accurately determined. The parameters of the process are the lower stress level, the number of failures at the lower stress level, the duration of test at the lower stress level (change time to higher stress level), the higher stress level, and the number of failures at the higher stress level and the duration of test at the higher stress level. In many cases, most of these parameters are predetermined based on experience and field failures. We intend to investigate efficient procedures to estimate most, if not all of these parameters under different operating conditions. The resultant optimum plan is verified through numerical example and sensitivity analysis.

Keywords: Operations Research; Uncertainty; Applied Probability; Stochastic Process; Optimization; Decision Science (search for similar items in EconPapers)
Date: 2007
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