Optimal experimental design practices are prominent in many applications. This paper proposes an alternate way of computing the information matrix, a key consideration in planning an accelerated life test. The generalized linear model approach allows optimal designs to be computed using iteratively weighted least-square solutions versus a maximum likelihood method. This approach is demonstrated with an assumed exponential distribution and allows the practitioner to observe the underlying structure of the optimal experimental design matrix and its relationship to important factors such as censoring and a nonlinear response function. Optimality criteria are discussed for both parameter estimation and prediction variance at an intended usage condition, which is typically outside the feasible accelerated test region.
A method of obtaining a set of closed-form solutions for reliability acceleration factors under time-varying temperature/humidity environments is presented. This paper outlines the procedure for generating such equations based on National Oceanographic and Atmospheric Administration climatic data. Results from Phoenix, Arizona are used as a case study. Predictions from these closed-form solutions are compared with traditional reliability assessments and exact solutions using raw ambient temperature and humidity values.
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