Maintenance planning under imperfect inspections of corroded pipelines

Sahraoui, Yacine; Khelif, Rabia; Chateauneuf, Alaa

doi:10.1016/j.ijpvp.2013.01.009

Cited by 79 publications

(37 citation statements)

References 20 publications

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“…7. BIC plot for eight covariance models for the environmental properties data: spherical models (1-2); diagonal models (3-6); ellipsoidal models (7)(8).…”

Section: Inference Of Actual Metal Lossmentioning

confidence: 99%

“…accuracy of the results is affected by many uncertainties (e.g. surface roughness, variation of original wall thickness, bulk modulus of pipeline material and temperature) [4][5][6][7]. Caleyo et al [5] reported that the ILI data are affected by systematic errors in the form of a constant bias (additive error) and/or errors in the form of a non-constant bias (multiplicative error).…”

Section: Introductionmentioning

confidence: 99%

“…the number of field verifications, which is a set of ILI data and its corresponding field measurements) in calibration results was mentioned, as a small sample size often makes the calibration quality questionable. Sahraoui et al [7] introduced the concept of probability of detection (PoD) and probability of false alarm (PFA) into their assessment approach but did not provide details for estimating PoD and PFA, which depend on the physical principles of the inspection method and the statistical characteristics of measurement noise [8,9]. Schoefs et al [10] assessed PoD and PFA by modeling the signal + noise distribution and the noise distribution based on field measurements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

et al. 2015

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“…7. BIC plot for eight covariance models for the environmental properties data: spherical models (1-2); diagonal models (3-6); ellipsoidal models (7)(8).…”

Section: Inference Of Actual Metal Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

et al. 2015

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“…The pipeline systems have been increasingly constructed to meet the rapid development of the oil and gas industry. Reliability assessment (Amirat, 2006), failure probability estimation (Xie, et al, 2008), and inspection of damage suffered for shock and corrosion (Sahraoui, et al, 2013) have been applied commonly for preventive replacement actions in pipelines to prevent incidents.…”

Section: Introductionmentioning

confidence: 99%

Preventive Replacement Policies with Aging Failure and Third-part Damage

Zhao

Al-Khalifa

Hamouda

2016

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems

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In general, corrosions, degrading the mechanical strength of pipelines gradually with its age in a stochastic way, is the predominant cause of pipeline leaks. In addition, the third-part damage is the leading cause of pipeline ruptures, which occurs randomly in a statistical sense. Naturally, corrective replacement (CR) is done immediately when the pipeline is subjected to a random failure. To reduce the failure probabilities, preventive replacement (PR) policies are scheduled to meet the failure due to aging and the third-part damage. We model three PR policies, using the renewal theory in reliability, and obtain their optimal solutions analytically to minimize respective replacement cost rates. Finally, numerical examples are given to compare these policies.

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“…Fault detection becomes a complicated task when pipelines are located in inaccessible places (Bhalla et al, 2005;Liu et al, 2013). Traditional inspection may be accurate, but depends on training and other human factors; and have a detection threshold where corrosion is not measurable (Sahraoui et al, 2013). Automated systems have the advantage of reducing the human intervention, and help the worker to make decisions (Iyer et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition by wavelet transforms using macro fibre composites transducers

González-Carrato

Márquez

Dimlaye³

et al. 2014

Mechanical Systems and Signal Processing

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This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. Wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated to cracks, leaks or corrosion. This issue may not be noticeable when temperatures are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarms levels as part of a structural health monitoring system.

show abstract

Maintenance planning under imperfect inspections of corroded pipelines

Cited by 79 publications

References 20 publications

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

Preventive Replacement Policies with Aging Failure and Third-part Damage

Pattern recognition by wavelet transforms using macro fibre composites transducers

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