It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Accelerated life testing (ALT) has been widely applied to many industries as an answer to the need of testing highly reliable products with long-term performance. Recently, step-stress accelerated life testing (SSALT) has been used in place of the constant stress ALT in order to further accelerate the failure process and to explore potential failure modes. The statistical inference procedure for SSALT models has not been thoroughly discussed so far. This study focuses on developing the maximum likelihood method and Bayesian approach to the model estimation based on the special data structure of the SSALT data with exponential failure time distribution. The improvement on the statistical precision of estimator and the reduction of required sample size are discussed.
This study consists of three main research contributions. First, a general Bayesian inference procedure is presented for a simple SSALT with type-II censoring and it is extended to a general SSALT containing multiple stress levels. For the Bayesian analysis the prior distribution of the parameters of life-stress function is formulated and the joint posterior distribution is derived via the Bayes conjugacy. Second, the statistical inference of exponential SSALT model is developed by utilizing techniques of generalized linear models (GLMs). For this GLM SSALT model, both maximum likelihood estimation and the Bayesian approach are discussed. The iterative weighted least square (IWLS) method is used for ML estimation, and Jeffreys' noninformative prior and the Markov chain Monte Carlo technique are applied for Bayesian estimation. Third, the GLM approach is extended to the Weibull SSALT model with interval censoring. Through a 2-stage iterative method and bootstrapping, the estimation procedure is implemented. The GLM technique provides a significant flexibility for the choice of computational tools. Numerical examples using industrial data are presented for the validation and illustration of the proposed method.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer