Probabilistic Model of Cumulative Damage in Pipelines Using Markov Chains

Casanova-del-Angel, Francisco; Flores-Méndez, Esteban; Cortes-Yah, Karina Guadalupe

doi:10.4236/jamp.2020.84048

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ossai and Davies [3] presented a pure birth Markov model to predict pit corrosion depth using negative binomial distribution for transition probability formulated based on different factors (pit depths, temperatures, CO2 partial pressures, pH, and flow rates). Casanova-del-Angel, et al [4] modeled the cumulative damage due to the propagation of internal corrosion (formulated with exponential functions) using the Markov chain to predict failure. It adopted Bernoulli trials and probability to make transition decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The MP has been adopted for modeling the pipeline corrosion process to predict the rate of degradation or failure [1][2][3][4]. Extensions of this method, such as the Markov decision process (MDP) and partially observable Markov decision process (POMDP), have also been implemented to integrate maintenance operation optimization with corrosion degradation process formulation as well as deterioration rate (failure) prediction [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

2023

View full text Add to dashboard Cite

This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and plan future inspection (monitoring) schemes and maintenance policy. A POMDP model is formulated as a decision-making support tool to effectively handle partially observed corrosion defect levels. It formulates states as the pipeline’s degradation level using a probability distribution. Inline inspection (ILI) methods estimate the latest state of the pipeline, which also defines the initial state of the optimization process. The set of actions comprises corrosion mitigation operations. The errors associated with the ILI method are used to construct the observation function for the model. The sum of inspection, maintenance operations, and failure costs for a given state and action are formulated as rewards. Numerical experiments are made based on data collected from the literature. The results showed that different policies, whether derived from solvers (theoretical) or determined from practical experience, can be compared to identify the best maintenance alternative using the model. It was also observed that the choice of the solvers is important since they affect the discounted rewards and the run time to obtain them. The model approximates the parameters and uncertainty associated with the propagation of corrosion, proficiency of inspection methods, and implementation of maintenance policies. Overall, it can be applied to improve the maintenance decision-making process for the oil and gas pipeline as it incorporates the stochastic features of the operation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

2023

View full text Add to dashboard Cite

show abstract

Probabilistic fatigue cumulative damage model considering the randomness of load and material properties

Lin,

Cao,

Yao

et al. 2023

Engineering Failure Analysis

View full text Add to dashboard Cite

Mathematical Modeling of Possibility Markov Chains by Possibility Theory

Uemura,

Kazuhisa,

Kita

2024

View full text Add to dashboard Cite

Statistical regression models are input-oriented estimation models that account for observation errors. On the other hand, an output-oriented possibility regression model that accounts for system fluctuations is proposed. Furthermore, the possibility Markov chain is proposed, which has a disidentifiable state (posterior) and a nondiscriminable state (prior). In this paper, we first take up the entity efficiency evaluation problem as a case study of the posterior non-discriminable production possibility region and mention Fuzzy DEA with fuzzy constraints. Next, the case study of the ex-ante non-discriminable event setting is discussed. Finally, we introduce the measure of the fuzzy number and the equality relation and attempt to model the possibility Markov chain mathematically. Furthermore, we show that under ergodic conditions, the direct sum state can be decomposed and reintegrated using fuzzy OR logic. We had already constructed the Possibility Markov process based on the indifferent state of this world. In this paper, we try to extend it to the indifferent event in another world. It should be noted that we can obtain the possibility transfer matrix by full use of possibility theory.

show abstract

Probabilistic Model of Cumulative Damage in Pipelines Using Markov Chains

Cited by 3 publications

References 8 publications

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

Probabilistic fatigue cumulative damage model considering the randomness of load and material properties

Mathematical Modeling of Possibility Markov Chains by Possibility Theory

Contact Info

Product

Resources

About