How to present and understand uncertainty of production assurance analyses with respect to data quality

Persskog-Lundtofte, C.; Pedersen, Simen

doi:10.1201/b17399-257

Cited by 4 publications

(10 citation statements)

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“…), which could have revealed, directly or indirectly, the evolution of its degradation state. The justification of such an assumption again relates to the specific, real case study considered in this paper (for further details, see [24]).…”

Section: Model Assumptionsmentioning

confidence: 98%

Semi-Markov Model for the Oxidation Degradation Mechanism in Gas Turbine Nozzles

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Martini

Mattafirri

et al. 2016

IEEE Trans. Rel.

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This paper presents the statistical characterization of the oxidation degradation mechanism affecting the nozzles of turbines operated in Oil and Gas utilities. The degradation mechanism is modeled as a four-state, continuous-time semi-Markov process with Weibull distributed transition times. Maximum likelihood estimation is used to infer the parameters of the model from an available set of field data, whereas a numerical approach to estimate the Fisher information matrix is used to characterize the uncertainty in the estimates. The estimates obtained are, then, utilized to compute the probabilities of occupying the four degradation states over time and the corresponding uncertainties. A case study is shown, dealing with real field data

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Section: Model Assumptionsmentioning

confidence: 98%

Semi-Markov Model for the Oxidation Degradation Mechanism in Gas Turbine Nozzles

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Martini

Mattafirri

et al. 2016

IEEE Trans. Rel.

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“…In addition to the considerations above, it is our experience that in some cases (e.g., the degradation behavior of some components of the turbines used in the oil & gas industry [35]), the experts of the maintenance engineering department have a relatively precise knowledge about both the stochastic process to be used to model the evolution of the degradation mechanisms, and the values of their parameters, too. In details, the choice of the process is usually justified by physical considerations; for example, the corrosion mechanism affecting the nozzles of gas turbines is often modeled as a discrete-state, continuous time process.…”

Section: Consider a Modelmentioning

confidence: 99%

Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems

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Zio

2015

IEEE Trans. Rel.

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The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting; this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values; ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization; and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry.

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“…If not provided by public databases, resorting to Fisher Information (Kendall et al, 1977;Di Maio et al, 2014a), the The standard deviation values of the transition rates of Table 2 are either provided by public databases or can be estimated by resorting to…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…Fisher Information (Kendall et al, 1977;Di Maio et al, 2014a). The procedure for this(that is hereafter described with reference to the RTD, whose failure rate standard deviation is not provided in (US: EPRI, 2008)) consists in:…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…The method adopted for this depends on the components modeling approach: for binary-state Markov Chain Models (MCMs), the variance of the transition failure rate is estimated by Fisher Information Matrix (Kendall et al, 1977;Di Maio et al, 2014a;Al-Dahidi et al, 2015;Kumar et al, 2015); for MSPM component models, the transition rates uncertainty is propagated and, therefore, estimated by MC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

Wang

Maio

Zio

2017

Reliability Engineering & System Safety

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Multi-State Physics Modeling (MSPM) provides a physics-based semiMarkov modeling framework for a more detailed reliability assessment. In this work, a three-loop Monte Carlo (MC) simulation scheme is proposed to operationalize the MSPM approach, quantifying and controlling the uncertainty affecting the system reliability model. The proposed MC simulation scheme involves three steps: i) the identification of the system components that deserve MSPM, ii) the quantification of the uncertainties in the MSPM component models and their propagation onto the system-level model, and iii) the selection of the most suitable modeling alternative that balances the computational demand for the system model solution and the robustness of the system reliability estimates.A Reactor Protection System (RPS) of a Nuclear Power Plant (NPP) is considered as case study for numerical evaluation.

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How to present and understand uncertainty of production assurance analyses with respect to data quality

Cited by 4 publications

References 0 publications

Semi-Markov Model for the Oxidation Degradation Mechanism in Gas Turbine Nozzles

Semi-Markov Model for the Oxidation Degradation Mechanism in Gas Turbine Nozzles

Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

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