Predictive Model Development to Perform Condition Assessment on Pipeline Networks

Rouhanizadeh, Behzad; Kermanshachi, Sharareh

doi:10.1061/9780784482445.004

Computing in Civil Engineering 2019 2019

DOI: 10.1061/9780784482445.004

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Predictive Model Development to Perform Condition Assessment on Pipeline Networks

Behzad Rouhanizadeh¹,

Sharareh Kermanshachi²

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2024

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Cited by 2 publications

References 11 publications

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Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Safaei,

Taghipour

2024

Quality & Reliability Eng

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Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations.

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Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Safaei,

Taghipour

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

Optimal Pipeline Maintenance Strategies in the United States: Stochastic Reliability Analysis of Gas Pipeline Network Failures

Kermanshachi

Rouhanizadeh

Cobanoglu

et al. 2020

J. Pipeline Syst. Eng. Pract.

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Predictive Model Development to Perform Condition Assessment on Pipeline Networks

Cited by 2 publications

References 11 publications

Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance

Optimal Pipeline Maintenance Strategies in the United States: Stochastic Reliability Analysis of Gas Pipeline Network Failures

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