A Bayesian‐based Reliability Estimation Approach for Corrosion Fatigue Crack Growth Utilizing the Random Walk

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.

[1]  Mohamed Chookah.  Structuring a probabilistic model for reliability evaluation of piping subject to corrosion-fatigue degradation , 2009 .

[2]  G. Grimmett,et al.  Probability and random processes , 2002 .

[3]  Richard P. Gangloff,et al.  Corrosion fatigue crack propagation in metals , 1990 .

[4]  R. P. Wei,et al.  Correlation between sustained-load and fatigue crack growth in high-strength steels , 1969 .

[5]  David De Leon,et al.  Effect of spatial correlation on the failure probability of pipelines under corrosion , 2005 .

[6]  R. Wei,et al.  Probability approach for prediction of corrosion and corrosion fatigue life , 1994 .

[7]  A. I. Pereguda,et al.  Probabilistic model of the reliability of a Du-500 pipeline , 1999 .

[8]  P. L Hall,et al.  Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study , 2003, Reliab. Eng. Syst. Saf..

[9]  Ouk Sub Lee,et al.  Reliability of Buried Pipeline Using a Theory of Probability of Failure , 2005 .

[10]  Mohammad Modarres,et al.  Reliability engineering and risk analysis : a practical guide , 2016 .

[11]  Tony L. Schmitz,et al.  Prediction of remaining useful life for fatigue-damaged structures using Bayesian inference , 2012 .

[12]  J. Yang,et al.  On statistical moments of fatigue crack propagation , 1983 .

[13]  Ming Gao,et al.  Reconsideration of the superposition model for environmentally assisted fatigue crack growth , 1983 .

[14]  Enrico Cagno,et al.  Using AHP in determining the prior distributions on gas pipeline failures in a robust Bayesian approach , 2000, Reliab. Eng. Syst. Saf..

[15]  Xuefei Guan,et al.  Maximum relative entropy-based probabilistic inference in fatigue crack damage prognostics , 2012 .

[16]  Mahesh D. Pandey,et al.  Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines , 2002 .

[17]  Mohammad Modarres,et al.  A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue , 2011, Reliab. Eng. Syst. Saf..

[18]  Xuefei Guan,et al.  Model selection, updating, and averaging for probabilistic fatigue damage prognosis , 2011 .

[19]  Homero Castaneda,et al.  Life Prediction Estimation of an Underground Pipeline Using Alternate Current Impedance and Reliability Analysis , 2004 .

[20]  D. A. Hansen,et al.  Statistical Modeling of Pitting Corrosion and Pipeline Reliability , 1990 .

[21]  Francisco Caleyo,et al.  A study on the reliability assessment methodology for pipelines with active corrosion defects , 2002 .

[22]  Mahesh D. Pandey,et al.  Probabilistic models for condition assessment of oil and gas pipelines , 1998 .

[23]  T. SHIBATA,et al.  Pitting corrosion as a stochastic process , 1976, Nature.

[24]  Xuefei Guan,et al.  Probabilistic fatigue damage prognosis using maximum entropy approach , 2012, J. Intell. Manuf..

[25]  Raphael T. Haftka,et al.  Uncertainty Reduction of Damage Growth Properties Using Structural Health Monitoring , 2010 .

[26]  R. P. Wei.  Environmental considerations for fatigue cracking , 2002 .

[27]  P. Sander,et al.  Bayesian Methods in Reliability , 1991 .

[28]  Francisco Caleyo,et al.  Markov Chain Models for the Stochastic Modeling of Pitting Corrosion , 2013 .

[29]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[30]  L. Joseph.  4. Bayesian data analysis (2nd edn). Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin (eds), Chapman & Hall/CRC, Boca Raton, 2003. No. of pages: xxv + 668. Price: $59.95. ISBN 1‐58488‐388‐X , 2004 .