In this dissertation, the problem of using accelerated degradation testing data for reliability estimation is studied and demonstrated. Simulation and analytical approaches have been investigated. By simulation which generates a large number of degradation paths, the reliability estimate of the product can be estimated using an empirical formation. This approach is general and has a great flexibility in estimating the reliability of a product regardless of the functional form of the degradation paths. However, it is time-consuming and sometimes can not provide efficient and accurate reliability estimates. Alternatively, the analytical approach may provide the closed-form expressions for reliability estimates for specific degradation process models. If the model fits, this approach is more accurate and efficient than the simulation approach. More importantly, when the closed-form solution exists, the optimal design of testing plans can be formulated and solved with the objective of either improving the accuracy of the reliability estimate or the accuracy of the economic loss. In addition to the statistical study of the reliability estimation, the optimal design of Accelerated Degradation Testing (ADT) plans has been investigated extensively. The objectives considered include minimizing the variance of single reliability estimate for the maintenance requirement, minimizing the weighted variances of multiple reliability estimates and minimizing the weighted economic loss associated with the reliability estimates considering multiple maintenance requirements. In the literature, this work is the first study regarding the optimal design of testing plans that considers maintenance requirements. By determining the optimal setting of decision variables such as the stress levels in the ADT experiments, the improvements in these objectives have been demonstrated using numerical examples. It can be seen that the novel methodology developed in this dissertation can significantly reduce the uncertainty of certain indices associated with the reliability estimates. This work is important in the area of reliability engineering as it indicates an efficient way of conducting accelerate degradation testing to verify the product's reliability and make management decisions under limited testing resources. / Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering. / "December 2006." / Includes bibliographic references (leaves 126-132)

To meet increasing competition, get products to market in the shortest possible time, and satisfy heightened customer expectations, products must be made more robust and fewer failures must be observed in a short development period. In this circumstance, assessing product reliability based on degradation data at high stress levels becomes necessary. This assessment is accomplished through accelerated degradation tests (ADT). These tests involve over stress testing in which instead of life product performance is measured as it degrades over time. Due to the role these tests play in determining proper reliability estimates for the product, it is necessary to scientifically design these test plans so as to save time and expense and provide more accurate estimates of reliability for a given number of test units and test time. In ADTs, several decision variables such as inspection frequency,the sample size, and the termination time at each stress level are important. In this research, an optimal plan is developed for the design of accelerated degradation test with a reciprocal Weibull degradation data using the mean time to failure (MTTF) as the minimizing criteria. A non linear integer programming problem is developed under the constraint that the total experimental cost does not exceed a pre-determined budget. The optimal combination of sample size, inspection frequency and the termination time at each stress level is found. A case example based on Light Emitting Diode (LED) example is used to illustrate the proposed method. Sensitivity analyses on the cost parameters and the parameters of the underlying probability distribution are performed to assess the robustness of the proposed method.