Risk-Based Maintenance: A New Vision for Asset Integrity Management

Montgomery, Randal L.; Serratella, Chris

doi:10.1115/pvp2002-1386

Cited by 12 publications

(9 citation statements)

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“…Later, the same methodology was applied to other structures such as ships and tankers (Soares and Garbatov 1996;Paik et al 2003), floating, production, storage and off-loading facilities (Lotsberg et al 1999;Goyet et al 2002), semi-submersibles, pipelines (Willcocks and Bai 2000;Desjardins 2002;Dey and Gupta 2001), process plants (Geary 2002;Montgomery and Serratella 2002;Kallen and Noortwijk 2005;Khan et al 2006), bridges (Frangopol et al 2004), and breakwaters (Noortwijk and Phajm 1996). The degradation mechanisms such as fatigue cracking and corrosion of steel and concrete structures were also considered (Faber 2002).…”

Section: Theoretical Backgroundmentioning

confidence: 96%

The selection of corrosion prior distributions for risk based integrity modeling

Thodi

Khan

Haddara

2008

Stoch Environ Res Risk Assess

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The deterioration of the condition of process plants assets has a major negative impact on the safety of its operation. Risk based integrity modeling provides a methodology to quantify the risks posed by an aging asset. This provides a means for the protection of human life, financial investment and the environmental damage from the consequences of its failures. This methodology is based on modeling the uncertainty in material degradations using probability distributions, known as priors. Using Bayes theorem, one may improve the prior distribution to obtain a posterior distribution using actual inspection data. Although the choice of priors is often subjective, a rational consensus can be achieved by judgmental studies and analyzing the generic data from the same or similar installations. The first part of this paper presents a framework for a risk based integrity modeling. This includes a methodology to select the prior distributions for the various types of corrosion degradation mechanisms, namely, the uniform, localized and erosion corrosion. Several statistical tests were conducted based on the data extracted from the literature to check which of the prior distributions follows data the best. Once the underlying distribution has been confirmed, one can estimate the parameters of the distributions. In the second part, the selected priors are tested and validated using actual plant inspection data obtained from existing assets in operation. It is found that uniform corrosion can be best described using 3P-Weibull and 3P-Lognormal distributions. Localized corrosion can be best described using Type1 extreme value and 3P-Weibull, while erosion corrosion can best be described using the 3P-Weibull, Type1 extreme value, or 3P-Lognormal distributions.

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Section: Theoretical Backgroundmentioning

confidence: 96%

The selection of corrosion prior distributions for risk based integrity modeling

Thodi

Khan

Haddara

2008

Stoch Environ Res Risk Assess

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show abstract

“…Although the work led to great differences in the results, various factors that affect the quality of the analyses were identified and the results of the analysis led to maintenance decisions. Montogomery and Serratela (2002) also developed a risk-based approach, based on the use of risk assessment and reliability analysis methodologies, to assist integrity management.…”

Section: Introductionmentioning

confidence: 99%

Recursive Operability Analysis as a decision support tool for Risk-Based Maintenance

Marmo¹,

Crivelletto²,

Starace³

2009

Journal of Loss Prevention in the Process Industries

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“…Another link in this chain of progress has been recently added by the introduction of a risk-based approach to maintenance. This approach has been suggested as a new vision for asset integrity management [1,14,15,17,18]. Risk-based maintenance strategies have been developed to provide a basis for not only taking the reliability of a system into consideration when making decisions regarding the type and the time for maintenance actions, but also to be able to take into consideration the risk that would result as a consequence of an unexpected failure [e.g.…”

Section: Introductionmentioning

confidence: 99%