The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Francisco Caleyo - One of the best experts on this subject based on the ideXlab platform.
-
Markov Chain Models for the Stochastic Modeling of Pitting Corrosion
Mathematical Problems in Engineering, 2013Co-Authors: A. Valor, Francisco Caleyo, J. C. Velázquez, L. Alfonso, J.m. HallenAbstract:The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the Transition Probability Function in a discrete pit depth space. The Transition Probability Function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the Transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.
-
markov chain model helps predict pitting corrosion depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the Transition Probability Function in a discrete space of pit depths. The Transition Probability Function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.Copyright © 2010 by ASME
-
Markov chain modelling of pitting corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the Transition Probability Function in a discrete pit depth space. The identification of the Transition Probability Function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
J.m. Hallen - One of the best experts on this subject based on the ideXlab platform.
-
Markov Chain Models for the Stochastic Modeling of Pitting Corrosion
Mathematical Problems in Engineering, 2013Co-Authors: A. Valor, Francisco Caleyo, J. C. Velázquez, L. Alfonso, J.m. HallenAbstract:The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the Transition Probability Function in a discrete pit depth space. The Transition Probability Function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the Transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.
-
markov chain model helps predict pitting corrosion depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the Transition Probability Function in a discrete space of pit depths. The Transition Probability Function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.Copyright © 2010 by ASME
-
Markov chain modelling of pitting corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the Transition Probability Function in a discrete pit depth space. The identification of the Transition Probability Function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
A Esquivelamezcua - One of the best experts on this subject based on the ideXlab platform.
-
markov chain model helps predict pitting corrosion depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the Transition Probability Function in a discrete space of pit depths. The Transition Probability Function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.Copyright © 2010 by ASME
A. Valor - One of the best experts on this subject based on the ideXlab platform.
-
Markov Chain Models for the Stochastic Modeling of Pitting Corrosion
Mathematical Problems in Engineering, 2013Co-Authors: A. Valor, Francisco Caleyo, J. C. Velázquez, L. Alfonso, J.m. HallenAbstract:The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the Transition Probability Function in a discrete pit depth space. The Transition Probability Function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the Transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.
-
markov chain model helps predict pitting corrosion depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the Transition Probability Function in a discrete space of pit depths. The Transition Probability Function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.Copyright © 2010 by ASME
-
Markov chain modelling of pitting corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the Transition Probability Function in a discrete pit depth space. The identification of the Transition Probability Function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
J. C. Velázquez - One of the best experts on this subject based on the ideXlab platform.
-
Markov Chain Models for the Stochastic Modeling of Pitting Corrosion
Mathematical Problems in Engineering, 2013Co-Authors: A. Valor, Francisco Caleyo, J. C. Velázquez, L. Alfonso, J.m. HallenAbstract:The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the Transition Probability Function in a discrete pit depth space. The Transition Probability Function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the Transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.
-
markov chain model helps predict pitting corrosion depth and rate in underground pipelines
2010 8th International Pipeline Conference Volume 4, 2010Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. Hallen, A EsquivelamezcuaAbstract:A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the Transition Probability Function in a discrete space of pit depths. The Transition Probability Function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.Copyright © 2010 by ASME
-
Markov chain modelling of pitting corrosion in underground pipelines
Corrosion Science, 2009Co-Authors: Francisco Caleyo, J. C. Velázquez, A. Valor, J.m. HallenAbstract:Abstract A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov’s forward equations for this type of Markov process is used to describe the Transition Probability Function in a discrete pit depth space. The identification of the Transition Probability Function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.