Extreme value analysis applied to pitting corrosion experiments in low carbon steel: Comparison of block maxima and peak over threshold approaches

Rivas, D.; Caleyo, F.; Valor, A.; Hallen, J.M.

doi:10.1016/j.corsci.2008.08.002

Cited by 84 publications

(40 citation statements)

References 10 publications

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“…Stable pits generally show stochastic behaviour [1,3] and are the focus of many researches. Pitting corrosion is initiated due to:…”

Section: Introductionmentioning

confidence: 99%

“…Although cathodic protection and other forms of coating have the ability of protecting marine infrastructures like pipelines from external pitting corrosion, ageing infrastructures exposed to marine environment have serious problem of pitting corrosion which can predominantly cause assets failures. Rivas et al [3] used block maxima and peak over threshold approach for extreme value analysis of laboratory simulated field data of buried carbon steel pipeline and concluded that the peak over threshold approach was more robust in estimating the maximum pit depth of the samples. In their own work, Valor et al [21] described pit initiation and propagation as a stochastic process of non-homogenous Poisson process and non-homogenous continuous time Markov process respectively.…”

Section: Introductionmentioning

confidence: 99%

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Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Ossai

Boswell

Davies

2016

Engineering Failure Analysis

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A continuous time non-homogenous linear growth pure birth Markov model was used to predict the future pit depth distribution of internally corroded oil and gas pipelines. A negative binomial distribution was used for calculating the transition probability functions of the pit depths whilst pit depths growth was estimated for low, moderate, high and severe pitting corrosion rates using field measured data of pit depths, temperatures, CO2 partial pressures, pH and flow rates. The Markov predicted results agreed well with field measured pit depth data from X52 grade pipeline and L-80 and N-80 grades offshore well tubing.

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“…Stable pits generally show stochastic behaviour [1,3] and are the focus of many researches. Pitting corrosion is initiated due to:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Ossai

Boswell

Davies

2016

Engineering Failure Analysis

View full text Add to dashboard Cite

show abstract

“…After showing statistically that the fitting of our experimental data is more relevant than any fitting by the most common laws (i.e. Normal [9], Lognormal [42], Weibull [43]) used in corrosion engineering literature to model pit depth distributions, the GLD model is considered to estimate the maximum pit depth. Then a second algorithm was computed to generate a high number of simulated datasets using the CBBM from the obtained GLD.…”

Section: Introductionmentioning

confidence: 99%

“…Pitting corrosion is an extremely dangerous form of localized corrosion since a perforation resulting from a single pit can cause complete in-service failure of installations like water pipes, heat exchanger tubes or oil tank used for example in chemical plants or nuclear power stations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The pits depth distribution is an important characteristic of the extent of such damage; the deeper the pits, the more dramatic the damage.…”

Section: Introductionmentioning

confidence: 99%

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A generic statistical methodology to predict the maximum pit depth of a localized corrosion process

et al. 2011

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This paper outlines a new methodology to predict accurately the maximum pit depth related to a localized corrosion process. It combines two statistical methods: the Generalized Lambda Distribution (GLD), to determine a model of distribution fitting with the experimental frequency distribution of depths, and the Computer Based Bootstrap Method (CBBM), to generate simulated distributions equivalent to the experimental one. In comparison with conventionally established statistical methods that are restricted to the use of inferred distributions constrained by specific mathematical assumptions, the major advantage of the methodology presented in this paper is that both the GLD and the CBBM enable a statistical treatment of the experimental data without making any preconceived choice neither on the unknown theoretical parent underlying distribution of pit depth which characterizes the global corrosion phenomenon nor on the unknown associated theoretical extreme value distribution which characterizes the deepest pits.Considering an experimental distribution of depths of pits produced on an aluminium sample, estimations of maximum pit depth using a GLD model are compared to similar estimations based on usual Gumbel and Generalized Extreme Value (GEV) methods proposed in the corrosion engineering literature. The GLD approach is shown having smaller bias and dispersion in the estimation of the maximum pit depth than the Gumbel approach both for its realization and mean. This leads to comparing the GLD approach to the GEV one. The former is shown to be relevant and its advantages are discussed compared to previous methods.

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A comprehensive study on the Bayesian modelling of extreme rainfall: A case study from Pakistan

Ahmad

Arshad

et al. 2021

Intl Journal of Climatology

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In this paper, the modelling of extreme rainfall is carried out in Pakistan by analysing annual daily maximum rainfall data via frequentist and Bayesian approaches. In frequentist settings, the parameters and return levels of the best fitted probabilistic model (i.e., generalized extreme value) are estimated using maximum likelihood and linear moments method. On the other side, under the Bayesian framework, the parameters and return levels are calculated both for noninformative and informative priors. This task is completed with the help of the Markov Chain Monte Carlo method using the Metropolis-Hasting algorithm. This study also highlights a procedure to build an informative prior through historical records of the underlying processes from other nearby weather stations. The findings attained from the Bayesian paradigm demonstrate that the posterior inference could be affected by the choice of past knowledge used for the construction of informative priors. Additionally, the best method for the modelling of extreme rainfall over the country is decided with the support of assessment measures. In general, the Bayesian paradigm linked with the informative priors offers an adequate estimations scheme in terms of accuracy as compared to frequentist methods, accounting for ambiguity in parameters and return levels. Hence, these findings are very helpful in adopting accurate flood protection measures and designing infrastructures over the country.

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Extreme value analysis applied to pitting corrosion experiments in low carbon steel: Comparison of block maxima and peak over threshold approaches

Cited by 84 publications

References 10 publications

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

A generic statistical methodology to predict the maximum pit depth of a localized corrosion process

A comprehensive study on the Bayesian modelling of extreme rainfall: A case study from Pakistan

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