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A Bayesian‐based Reliability Estimation Approach for Corrosion Fatigue Crack Growth Utilizing the Random Walk
Author(s) -
Yang Lechang,
Zhang Jianguo,
Guo Yanling,
Wang Pidong
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1954
Subject(s) - reliability (semiconductor) , bayesian inference , bayesian probability , cumulative distribution function , reliability engineering , computer science , inference , probability density function , corrosion fatigue , function (biology) , corrosion , engineering , mathematics , statistics , artificial intelligence , materials science , power (physics) , physics , quantum mechanics , evolutionary biology , metallurgy , biology
Structural health monitoring enables corrosion fatigue damage for in‐service structures to be evaluated and prognosis health management to perform. In this paper, a Bayesian inference method using random walks is implemented to estimate the reliability of structures such as the pipelines subjected to repeated pressurization cycles and corrosive agents. The proposed method eliminates the intermediate step in updating process and computes the cumulative distribution function instead of calculating probability density function of individual parameters in conventional ones, which is affordable for a routine program, especially convenient for practical engineering use in the field. As taking all relevant random variables into account, this approach could significantly reduce uncertainties associated. For illustration and validation purpose, both numerical and practical examples are demonstrated in details. Copyright © 2016 John Wiley & Sons, Ltd.