A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

Chookah, Mohamed; Nuhi, Mohammad; Modarres, Mohammad

doi:10.1016/j.ress.2011.07.007

Cited by 69 publications

(34 citation statements)

References 19 publications

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“…2,[21][22] Furthermore, pit depth growth can be determined using limited data via extreme value analysis, which depends on the maximum pit depth at an exposure time for extrapolating future pit depth distribution over a long-time exposure. 23 Researchers such as Mohd and Paik 17 investigated the relationship between internal pit depth growth and age of offshore pipelines statistically and showed the correlation between age of pipelines and Weibull scale and shape parameters. Although the work did not show the reliability of these pipelines at various ages, it is a good outlook for the progression of pit depth with time.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Internal Pit Depth Growth and Reliability of Aged Oil and Gas Pipelines— A Monte Carlo Simulation Approach

Ossai¹,

Boswell²,

Davies³

2015

CORROSION

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To estimate the internal pit depth growth and reliability of aged oil and gas pipelines, a Monte Carlo simulation approach was adopted. The average maximum pit depths of corroded pipelines were correlated with the operating parameterstemperature, CO 2 partial pressure, pH, flow rate, sulfate ion concentration, chloride ion concentration, water cut, and wall shear stress via a multivariate regression analysis. Poisson square wave model was used to predict the time lapse of the pit depth growth using the statistical best fit of the maximum pit depth and operating parameters as boundary conditions. Weibull probability function was used to determine the failure intensity and survivability of the pipelines for different distribution types, whereas inspection data from a magnetic flux leakage in-line inspected transmission pipeline were used to test the application of the model. The future pit depth distribution, survivability, and failure rate of this transmission pipeline were also determined, with the result showing that the model is vital for future internal pit depth growth and reliability estimation from single field inspection data.

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Section: Introductionmentioning

confidence: 99%

Estimation of Internal Pit Depth Growth and Reliability of Aged Oil and Gas Pipelines— A Monte Carlo Simulation Approach

Ossai¹,

Boswell²,

Davies³

2015

CORROSION

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“…represents crack generation because of pitting corrosion, and the second term shows the corrosion‐enhanced fatigue crack growth. Chookah's model is adopted for its closed agreement with Wei's benchmark model and consistently matches for plausible value to experimental data, indicating a successful proposal . Figure shows a series of curves (crack size a i versus cycles N ) for typical corrosion degradation procedure from pitting corrosion to corrosion fatigue crack growth.…”

Section: The Physical Model For Corrosion Degradationmentioning

confidence: 99%

“…According to Bayes' rule, the posterior distribution is proportional to the product of the prior and likelihood functions. The likelihood equation of the crack size is assumed to follow a lognormal distribution as follows:

f ((), a) = L N ((),, μ_{i}, s_{i})

where μ i is the log mean of crack size distribution. By using Eq.…”

Section: Bayesian Inference Using Random Walksmentioning

confidence: 99%

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

Yang

Zhang

Guo

et al. 2016

Quality & Reliability Eng

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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.

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“…Thus the PoF model tend to develope into the Probabilistic Physics of failure(PPoF), which describes the probability function relation between the external environment and the time of failure. [12][13][14] In this paper, 1) the coalescence of two adjucent coplanlar semiellipse cracks is researched via simulation method; 2) probabilistic failure physics(PPoF) models for cracks before and after coalescence are established; 3) life distribution of probability density curves are obtaind by Monte Carlo simulation.…”

Section: Introducitionmentioning

confidence: 99%

Research on coalescence and probabilistic failure physics modeling of coplanar semiellipse cracks

Sun

et al. 2018

MATEC Web Conf.

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Abstract. Multiple fatigue cracks, causing transient fatigue fracture and resulting in great loss of life and property, are very commen in engineering structures. In order to predict the fatigue life of engineering alloys more accurately, the interaction and coalescence of adjacent cracks was investigated and the four stages of cracks growth were discussed in this paper. The SIF(stress intensity factor) value was found to growth rapidly in the ligament failure, and simulation method was used to find out the coalescence point. Then, the Paris' law was chosen to discribe the crack growth process. Taking random factors like structure size, material properties, environment stress parameter into account, the probability physical of failure model of alloys can predict the failure life more accurate. Accordingly, the uncertain parameters of the Paris' law were updated via Bayesian update method. The posterior distributions of uncertain parameters were obtained by the MCMC-Gibbs sampling. Finally, the probabilistic failure life models before and after coalescence were established by Monte Carlo simulation method. The probabilistic modeling method, which can greatly reduces the historical data or experimental data needed for the reliability evaluation of small sample products, can also be applied to other aspect of physic of failure , effectively saving time and cost. IntroducitionFatigue failures, which occures at stress below the tensile strength or yield strength of alloys is hard to be detected. Since most engineering components are or similarly are subjected to alternating stress. Multiple fatigue cracks causing transient fatigue fracture and resulting in great loss of life and property, are very commen in engineering structures. Crack mainly initiats in localized areas of stress concentratin or material defects [1,2]. Then these initial cracks grow together by interacting with each other, meanwhile shortening the fatigue life with an accelerated crack growth rate [3]. Interaction and coalescence processes of adjacent cracks is the key issue in the study of coplanar fatigue cracks.Various theories have been propounded to assess the growth of adjacent surface cracks growth. The British Standard Institution[4] proposed a prediction method assuming cracks to be indepentend. However this idealize simple model mainly considering the geometric condition of cracks, turned out to be too conservative [5]. Iida [6] assumed that when the cracks touch, a semi-elliptical crack immediately drawn through the outer tips of the two adjucent cracks and through the deepest point of the deeper crack. The study neglected the interaction in calculation between adjucent cracks(the interaction factors γA to F =1) and proposed a simple realistic prediction method. However the a Corresponding

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A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

Cited by 69 publications

References 19 publications

Estimation of Internal Pit Depth Growth and Reliability of Aged Oil and Gas Pipelines— A Monte Carlo Simulation Approach

Estimation of Internal Pit Depth Growth and Reliability of Aged Oil and Gas Pipelines— A Monte Carlo Simulation Approach

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

Research on coalescence and probabilistic failure physics modeling of coplanar semiellipse cracks

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