Integration of computation and testing for reliability estimation |
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Authors: | Ruoxue Zhang Sankaran Mahadevan |
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Affiliation: | Department of Civil and Environmental Engineering, Vanderbilt University, Box 6077-B, 306 Jacobs Hall, Nashville, TN 37235, USA |
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Abstract: | This paper develops a methodology to integrate reliability testing and computational reliability analysis for product development. The presence of information uncertainty such as statistical uncertainty and modeling error is incorporated. The integration of testing and computation leads to a more cost-efficient estimation of failure probability and life distribution than the tests-only approach currently followed by the industry. A Bayesian procedure is proposed to quantify the modeling uncertainty using random parameters, including the uncertainty in mechanical and statistical model selection and the uncertainty in distribution parameters. An adaptive method is developed to determine the number of tests needed to achieve a desired confidence level in the reliability estimates, by combining prior computational prediction and test data. Two kinds of tests — failure probability estimation and life estimation — are considered. The prior distribution and confidence interval of failure probability in both cases are estimated using computational reliability methods, and are updated using the results of tests performed during the product development phase. |
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Keywords: | Product reliability Reliability analysis Life prediction Reliability testing Uncertainty modeling Bayesian analysis Confidence bounds |
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