Maintainability analysis considering time-dependent and time-independent covariates

Barabadi, Abbas; Barabady, Javad; Markeset, Tore

doi:10.1016/j.ress.2010.08.007

Cited by 89 publications

(67 citation statements)

References 20 publications

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“…Equation (20) implies that prior to its current cycle + 1, the component has accumulated a total uptime of ∑ =1…”

Section: Perfect and Minimal Repair Assumptionmentioning

confidence: 99%

“…More generally, advanced PHMs have been proposed [15][16][17][18][19] to analyse the hazard rate behaviour in the presence of dynamically evolving covariates, such as the weather conditions, including changes in wind speed, occurrence of storms and lightning events, etc. [20][21][22]. Although these approaches seem attractive for their potential of providing more precise estimates of RAM, their application to practical Arctic offshore O&G case studies is still prevented from the lack of reliability and operating data for proper setting of the RAM models.…”

Section: Introductionmentioning

confidence: 99%

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Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Naseri

Baraldi²,

Compare³

et al. 2016

Reliability Engineering & System Safety

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We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions. Keywords: Dynamic weather conditions, failure rate, repair rate, equivalent age, preventive maintenance, availability, Monte Carlo simulation, oil and gas, Arctic offshore. , ′ ); = 1, … , ; 0 ′ = 0, partitioning the time horizon, during which the weather conditions remain unchanged at an intensity level of . In this study, the time interval length is taken to be equal to a day. Acronyms ALMReliability of a component operating under static weather conditions at WIL State of the system at WIL , = 0, … , ; = 1 and = 0 refer to the functioning and faulty states, respectively. Waiting downtime before commencing CM tasks in the base area, which includes the time required to shut down the unit, issue the work orders, wait for the spare parts, and start up the unit after repair.Weather element referring to either minimum daily air temperature or maximum daily wind speed, i.e., ∈ { , ′ } ( ) Maximum wind speed during in km/hr ′ ( ) Box-Cox transformed wind speed at the th day The factor by which the weather-dependent factor , changes due to modifications to plant design The factor by which the weather-dependent factor , changes due to modifications to the comfort of maintenance crew, or modifications to the plant design resulting in changes in component active repair time 0 Weibull shape parameter, estimated using the life data collected in the base area (i.e., normal weather conditions), partitioning the time horizon; during each time interval the weather conditions are assumed constant Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , with 0 = 1, which accounts for the reductions in TTFs. Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the reductions in TTFs Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs * Modified weather-dependent multiplicative factor corresponding...

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“…Equation (20) implies that prior to its current cycle + 1, the component has accumulated a total uptime of ∑ =1…”

Section: Perfect and Minimal Repair Assumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Naseri

Baraldi²,

Compare³

et al. 2016

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…The literature on maintainability analysis has mostly concentrated on estimating the (mean) time to repair, using either statistical methods or expert-based assessment (see, e.g., Barabadi et al, 2011;Moreu De Leon et al, 2012). A quantification 6 of both resource demand and task time are very useful for decision making.…”

Section: Maintenance Task Analysismentioning

confidence: 99%

Defining line replaceable units

Puig

Basten

2015

European Journal of Operational Research

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“…Several works have explored a Weibull baseline hazard function in CPHM for maintenance decision making strategies, whether they be replacement decisions (Jardine, Banjevic, Makis, & Ennis, 2001); Wu & Ryan, 2011), inspection interval decisions (Chen, Chen, Li, Zhou, & Sievenpiper, 2011), procurement decisions (Louit, Pascual, Banjevic, & Jardine, ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= dê~Çì~íÉ=pÅÜççä=çÑ=_ìëáåÉëë=C=mìÄäáÅ=mçäáÅó -6 -k~î~ä=mçëíÖê~Çì~íÉ=pÅÜççä= 2011), and maintainability decisions (Barabadi, Barabady, & Markeset, 2011). Yacout et al (2007) suggested that further research should be done for preventative maintenance that benefits from condition data with the power to schedule in advance.…”

Section: (2)mentioning

confidence: 99%

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Barker¹

2013

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The aim of this research work is to develop a framework for making coordinated acquisitions decisions, integrating: (i) schedules of maintenance based on the condition of system components, and (ii) trigger acquisition of multiple vendor maintenance, repair, and overhaul (MRO) supplies and services. To do so, we develop a multi-objective optimization maintenance/acquisition scheduling algorithm where tradeoffs are made between two competing objectives: maximizing conditionbased component reliability while maximizing the sensitivity of condition-based reliability to changes in the component's condition (e.g., degradation). This scheduling algorithm integrates with an acquisition algorithm to addresses vendor lead-time. Case studies broadly inspired by Tinker Air Force Base, the largest Air Force MRO hub in the US, in Oklahoma City, OK, illustrate the framework. Keywords

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Maintainability analysis considering time-dependent and time-independent covariates

Cited by 89 publications

References 20 publications

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Defining line replaceable units

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

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