Probabilistic Methods for Predicting the Remaining Life of Offshore Pipelines

Aljaroudi, Alireda; Thodi, Premkumar; Akintürk, Ayhan; Khan, Faisal; Paulin, Mike

doi:10.1115/1.4036217

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…It evaluates the performance function by using the first order Taylor series expansion of the limit state function (LSF) at the mean value. This method is used when the performance function is linear having statistically independent, normally distributed and noncorrelated random variables X 0 i s. Performance function can be defined as [3,5,10]:…”

Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

“…Then the probability of failure (P f ) can be computed as: Figure 1 shows the probability density function (PDF) of the random variable Z, as it can be depicted from the figure that the probability of failure comprises the shaded area where Z < 0. The probability of failure is expressed as [3,5,10]:…”

Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

“…Alternatively, the performance function Z can be formulated in terms of many random variables designated by a vector X as [3,5,10]:…”

Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

“…The pipeline wall thickness follows normal distribution with a mean and standard deviation values of 14 mm and 4.7 respectively. The critical pipeline wall thickness has been determined to be 80% of the wall thickness [3]. A summary of the relevant information pertaining to the pipeline corrosion is presented in the net table, Table 5.…”

Section: Part Bmentioning

confidence: 99%

“…The owner of the pipeline decides not to repair the corrosion and wants to know if the pipeline can survive for the next 14 years without causing a leakage. It has been decided that in order to be in the safe side the maximum acceptable probability of failure has been set to 1e4 [3].…”

Section: Part Bmentioning

confidence: 99%

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Probabilistic Modeling of Failure

Aljaroudi¹

2019

Failure Analysis

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Failure of a system or a component of a system is and has been a major concern to systems' operators and owners. Failure could be traced back to different causes and may take different forms and shapes. It may result from software malfunction, hardware degraded performance, human error, sabotage, environmental as well as other external factors. There are various techniques found in the literature that can assist in the analysis of failure. These techniques comprise deterministic and probabilistic techniques. Deterministic techniques ignore the variability and uncertainties of the variables in the analysis which may lead to unsatisfactory and inaccurate results. While probabilistic techniques produce accurate and an allinclusive result because they incorporate the variabilities and uncertainties in the analysis. The focus of this chapter is to present commonly used probabilistic failure analysis techniques and their mathematical derivations. Examples to enhance the understanding of the concept of failure analysis are also presented.

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Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

“…Alternatively, the performance function Z can be formulated in terms of many random variables designated by a vector X as [3,5,10]:…”

Section: First Order Second Moment (Fosm)mentioning

confidence: 99%

Section: Part Bmentioning

confidence: 99%

Section: Part Bmentioning

confidence: 99%

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Probabilistic Modeling of Failure

Aljaroudi¹

2019

Failure Analysis

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Optimal inspection and maintenance plans for corroded pipelines

Wang¹,

Su²,

Xie³

2021

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)

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Optimizing inspection plan for corroded pipeline with considering imperfect maintenance

Wang

Xie

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Metal-loss corrosion may result in pipeline’s failure and expensive downtime loss. Thus, to keep the pipeline’s normal operation, it is crucial to draw up scientific inspection and maintenance plans. This study is to optimize the inspection plan for corroded pipeline. Limit state functions are established for the corrosion leakage and burst respectively, and the pipeline’s failure probability is obtained with Monte Carlo simulation. The pipeline’s failure probability is further evaluated by the copula function and with considering the correlation of different failure modes. Moreover, a hybrid failure rate model is developed to update the pipeline’s failure probability, where the age reduction factor and failure rate increase factor are adopted, and imperfect maintenance is taken into account. On this basis, an optimization model is established with the objective to minimize the total maintenance cost, and genetic algorithm is applied to optimize the inspection plans. A case study is conducted, and the optimal results are analyzed according to acceptable maximum failure probability characteristics for the areas with different risk levels. The results show that periodic inspection and perfect maintenance are suitable for high-risk areas, and the proposed inspection plan is more suitable for corroded pipeline in low or medium-risk areas.

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Probabilistic Methods for Predicting the Remaining Life of Offshore Pipelines

Cited by 3 publications

References 4 publications

Probabilistic Modeling of Failure

Probabilistic Modeling of Failure

Optimal inspection and maintenance plans for corroded pipelines

Optimizing inspection plan for corroded pipeline with considering imperfect maintenance

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