Prediction of Corroded Pipeline Performance Based on Dynamic Reliability Models

Reza Aulia (Corresponding Author), Henry Tan, Srinivas Sriramula

Research output: Contribution to journalConference article

2 Downloads (Pure)

Abstract

This paper focusses on developing an initial model for dynamic reliability analysis to predict the aging pipeline performance due to corrosion. The corrosion failure mechanism and the associated data requirements are identified by combining outputs from the literature and project experiences. Bayesian networks (BN) are developed to manage and overcome data uncertainties and the dynamic consideration is utilized to introduce time function into the model to accommodate the time-dependent variables. Several parameters are considered in the model development, such as pipeline content, size and material grade, environmental conditions, operational conditions, internal and external corrosion rates mitigation methods and in-line inspection data on corrosion rates. The application of the proposed model to an industrial case study is presented in this paper, along with the basic event prioritization analysis using the sensitivity approach. The proposed dynamic Bayesian model provides an efficient option for reliability assessment, to predict the future condition of the corroded pipeline based on the current and historical data, leading to rational risk assessment.

Original languageEnglish
Pages (from-to)518-523
Number of pages6
JournalProcedia CIRP
Volume80
Early online date4 May 2019
DOIs
Publication statusPublished - 2019
Event26th CIRP Conference on Life Cycle Engineering, LCE 2019 - West Lafayette, United States
Duration: 7 May 20199 May 2019

Fingerprint

Pipelines
Corrosion rate
Corrosion
Bayesian networks
Reliability analysis
Risk assessment
Aging of materials
Inspection

Keywords

  • Bayesian network
  • Life extension
  • Reliability analysis
  • Subsea pipeline

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Prediction of Corroded Pipeline Performance Based on Dynamic Reliability Models. / Aulia, Reza (Corresponding Author); Tan, Henry; Sriramula, Srinivas.

In: Procedia CIRP, Vol. 80, 2019, p. 518-523.

Research output: Contribution to journalConference article

@article{ec46bbd53d3f4b1b86e8fb536ed33403,
title = "Prediction of Corroded Pipeline Performance Based on Dynamic Reliability Models",
abstract = "This paper focusses on developing an initial model for dynamic reliability analysis to predict the aging pipeline performance due to corrosion. The corrosion failure mechanism and the associated data requirements are identified by combining outputs from the literature and project experiences. Bayesian networks (BN) are developed to manage and overcome data uncertainties and the dynamic consideration is utilized to introduce time function into the model to accommodate the time-dependent variables. Several parameters are considered in the model development, such as pipeline content, size and material grade, environmental conditions, operational conditions, internal and external corrosion rates mitigation methods and in-line inspection data on corrosion rates. The application of the proposed model to an industrial case study is presented in this paper, along with the basic event prioritization analysis using the sensitivity approach. The proposed dynamic Bayesian model provides an efficient option for reliability assessment, to predict the future condition of the corroded pipeline based on the current and historical data, leading to rational risk assessment.",
keywords = "Bayesian network, Life extension, Reliability analysis, Subsea pipeline",
author = "Reza Aulia and Henry Tan and Srinivas Sriramula",
note = "This research is sponsored by the Ministry of Finance of the Republic of Indonesia through the Indonesia Endowment Fund for Education (LPDP)(grant number: PRJ-4202 /LPDP.3/2016).",
year = "2019",
doi = "10.1016/j.procir.2019.01.093",
language = "English",
volume = "80",
pages = "518--523",
journal = "Procedia CIRP",
issn = "2212-8271",
publisher = "Elsevier",

}

TY - JOUR

T1 - Prediction of Corroded Pipeline Performance Based on Dynamic Reliability Models

AU - Aulia, Reza

AU - Tan, Henry

AU - Sriramula, Srinivas

N1 - This research is sponsored by the Ministry of Finance of the Republic of Indonesia through the Indonesia Endowment Fund for Education (LPDP)(grant number: PRJ-4202 /LPDP.3/2016).

PY - 2019

Y1 - 2019

N2 - This paper focusses on developing an initial model for dynamic reliability analysis to predict the aging pipeline performance due to corrosion. The corrosion failure mechanism and the associated data requirements are identified by combining outputs from the literature and project experiences. Bayesian networks (BN) are developed to manage and overcome data uncertainties and the dynamic consideration is utilized to introduce time function into the model to accommodate the time-dependent variables. Several parameters are considered in the model development, such as pipeline content, size and material grade, environmental conditions, operational conditions, internal and external corrosion rates mitigation methods and in-line inspection data on corrosion rates. The application of the proposed model to an industrial case study is presented in this paper, along with the basic event prioritization analysis using the sensitivity approach. The proposed dynamic Bayesian model provides an efficient option for reliability assessment, to predict the future condition of the corroded pipeline based on the current and historical data, leading to rational risk assessment.

AB - This paper focusses on developing an initial model for dynamic reliability analysis to predict the aging pipeline performance due to corrosion. The corrosion failure mechanism and the associated data requirements are identified by combining outputs from the literature and project experiences. Bayesian networks (BN) are developed to manage and overcome data uncertainties and the dynamic consideration is utilized to introduce time function into the model to accommodate the time-dependent variables. Several parameters are considered in the model development, such as pipeline content, size and material grade, environmental conditions, operational conditions, internal and external corrosion rates mitigation methods and in-line inspection data on corrosion rates. The application of the proposed model to an industrial case study is presented in this paper, along with the basic event prioritization analysis using the sensitivity approach. The proposed dynamic Bayesian model provides an efficient option for reliability assessment, to predict the future condition of the corroded pipeline based on the current and historical data, leading to rational risk assessment.

KW - Bayesian network

KW - Life extension

KW - Reliability analysis

KW - Subsea pipeline

UR - http://www.scopus.com/inward/record.url?scp=85067179707&partnerID=8YFLogxK

UR - https://linkinghub.elsevier.com/retrieve/pii/S2212827119300952

UR - http://www.mendeley.com/research/prediction-corroded-pipeline-performance-based-dynamic-reliability-models

U2 - 10.1016/j.procir.2019.01.093

DO - 10.1016/j.procir.2019.01.093

M3 - Conference article

VL - 80

SP - 518

EP - 523

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

ER -