A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework

Singh, Maneesh; Markeset, Tore

doi:10.1016/j.engfailanal.2009.02.003

Cited by 102 publications

(32 citation statements)

References 11 publications

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“…Singh and Markeset [26] tried to establish an RBI program for pipes, using a fuzzy logic framework estimating the rate of CO 2 corrosion in carbon steel pipes and taking into account the efficiency of inspection as a fuzzy variable where the goal is to estimate the rate of corrosion and use it to develop a risk-based inspection program.…”

Section: Typical Applications Of the Rbi Methodologymentioning

confidence: 99%

Establishment of optimal physical assets inspection frequency based on risk principles

Ferreira¹

2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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Section: Typical Applications Of the Rbi Methodologymentioning

confidence: 99%

Establishment of optimal physical assets inspection frequency based on risk principles

Ferreira¹

2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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“…Therefore, current prediction methods in the oil and gas domain are still unable to address the problem of having multiple damage mechanisms due the limitations of methods used and researchers chose to target only the more critical damage mechanisms in their studies [5]. The fact that over 60 damage mechanisms exist to cause corrosion [44] should not be overlooked and corrosion prediction for a particular pipeline must be done while taking into account the specific damage mechanism which is affecting the pipeline.…”

Section: E Significance Of Studymentioning

confidence: 99%

“…Damage mechanisms eventually lead to corrosion and the pipeline is subjected to leakages and ruptures, resulting in major financial losses and poses substantial health, environment and safety hazards to the surrounding ecosystem [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Lee¹,

Aziz²,

Jaafar³

2017

ijacsa

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Abstract-Artificial Neural Network (ANN) design has long been a complex problem because its performance depends heavily on the network topology and algorithm to train the set of synaptic weights. Particle Swarm Optimization (PSO) has been the favored optimization algorithm to complement ANN, but a thorough literature study has shown that there are gaps with current approaches which integrate PSO with ANN, including the optimization of network topology and the unreliable weight training process. These gaps have caused inferior effect on critical Artificial Intelligence (AI) applications and systems, particularly when predicting plant machinery and piping failure due to corrosion. The problem of corrosion prediction in the oil and gas domain remains unanswered due to the lack of a flexible prediction method which targets specific damage mechanisms that caused corrosion. This paper proposes a hybrid prediction method known as the Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN), which integrates several layers of PSO to optimize different parameters of the ANN. The multilayered PSO enables the method to optimize the network topology and train the set of synaptic weights at the same time using a hierarchical optimization approach. Through detailed discussion and literature study, the damage mechanism focused in this research is the CO 2 corrosion and the dataset for this research is obtained from the NORSOK empirical model. The proposed AMPSONN method is tested against BP, MPSO and PSOBP methods on an industrial corrosion dataset for different test conditions. The results showed that AMPSONN performs best on all three problems, exhibiting high classification accuracies and time efficiency.

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“…Furthermore, due to measurement imperfection, it is hardly possible to quantify the degree of corrosion in a crisp manner. Fuzzy inference or fuzzy logic is one of promising way to dealing with uncertainty and imprecision appeared in real world problems where no precise boundaries are found [6]. Fuzzy approach has an advantage in that subjective knowledge is easily incorporable with well-developed analytical models.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

Kim¹,

Choe²,

Kim³

et al. 2016

Journal of Korean Institute of Intelligent Systems

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Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

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A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework

Cited by 102 publications

References 11 publications

Establishment of optimal physical assets inspection frequency based on risk principles

Establishment of optimal physical assets inspection frequency based on risk principles

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

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