Evaluating maintenance policies by quantitative modeling and analysis

Zio, Enrico; Compare, Michele

doi:10.1016/j.ress.2012.08.002

Cited by 110 publications

(79 citation statements)

References 30 publications

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“…The aim of performing PM tasks can be reducing the number of failures, improving system safety, increasing plant production rate, and reducing unplanned system downtimes [61][62][63]. Among different approaches to PM tasks, this study considers a calendar-based scheduled PM, whose impact on system performance is assessed in terms of plant availability and expected number of failures.…”

Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

“…Among different approaches to PM tasks, this study considers a calendar-based scheduled PM, whose impact on system performance is assessed in terms of plant availability and expected number of failures. A number of approaches have been propounded in the literature to cope with the issue of setting a PM policy [62,64]. In this study, the PM is of an overhaul type, taking place every years, for a period of 3 weeks considering normal weather conditions.…”

Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

“…This type of PM affects plant availability in two ways: i) it reduces plant availability by imposing shutdowns every years for a period of 3 weeks, and ii) it improves plant availability by removing all the previous degradations from components' history, and thus reducing system hazard rates. Therefore, determining the optimal maintenance interval (i.e., the time spans between two successive maintenance actions) plays a key role in maximising the overall profitability of the plant taking account of, namely, PM costs, CM costs, plant production, and safety issues [61][62][63]. This study includes only the plant availability and expected number of failures as the criteria for optimising .…”

Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

Section: Plant Availability Under Scheduled Pmmentioning

confidence: 99%

See 1 more Smart Citation

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

Naseri

Baraldi²,

Compare³

et al. 2016

Reliability Engineering & System Safety

Self Cite

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“…To anticipate failures, predictive maintenance approaches are being developed, based on the assessment of the actual equipment degradation condition and on the prediction of its evolution for setting the optimal time for maintenance [22,23,44,48,49]. The underlying concept is that of failure prognostics, i.e., predicting the Remaining Useful Life (RUL) of the equipment, defined as the amount of time it will continue to perform its function according to the design specifications.…”

Section: Introductionmentioning

confidence: 99%

A belief function theory based approach to combining different representation of uncertainty in prognostics

Baraldi

Mangili

Zio

2015

Information Sciences

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In this work, we consider two prognostic approaches for the prediction of the remaining useful life (RUL) of degrading equipment. The first approach is based on Gaussian Process Regression (GPR) and provides the probability distribution of the equipment RUL; the second approach adopts a Similarity-Based Regression (SBR) method for the RUL prediction and belief function theory for modeling the uncertainty on the prediction. The performance of the two approaches is comparable and we propose a method for combining their outcomes in an ensemble. The least commitment principle is adopted to transform the RUL probability density function supplied by the GPR method into a belief density function. Then, the Dempster's rule is used to aggregate the belief assignments provided by the GPR and the SBR approaches. The ensemble method is applied to the problem of predicting the RUL of filters used to clean the sea water entering the condenser of the boiling water reactor (BWR) in a Swedish nuclear power plant. The results by the ensemble method are shown to be more satisfactory than that provided by the individual GPR and SBR approaches from the point of view of the representation of the uncertainty in the RUL prediction.

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“…Multi-state degradation modelling has recently received considerable attention in the domain of reliability and maintenance engineering [2], [7], [36], [45], [46], as it pragmatically allows setting advanced maintenance paradigms such as Condition-Based Maintenance (CBM) and Predictive Maintenance [62], [63]. In practice, the parameters governing the stochastic transitions among the states of these models are first estimated, based on the available data; then, the degradation model is embedded into the maintenance model to estimate the performance indicators of interest (e.g., unavailability [34], [47], profitability [1], quality in production [11], total costs, risk [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithms for condition-based maintenance optimization under uncertainty

Compare¹,

Martini

Zio

2015

European Journal of Operational Research

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This paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept.The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant.

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Evaluating maintenance policies by quantitative modeling and analysis

Cited by 110 publications

References 30 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

A belief function theory based approach to combining different representation of uncertainty in prognostics

Genetic algorithms for condition-based maintenance optimization under uncertainty

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