8 research outputs found

    Fuzzy Reliability Assessment of Systems with Multiple Dependent Competing Degradation Processes

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    International audienceComponents are often subject to multiple competing degradation processes. For multi-component systems, the degradation dependency within one component or/and among components need to be considered. Physics-based models (PBMs) and multi-state models (MSMs) are often used for component degradation processes, particularly when statistical data are limited. In this paper, we treat dependencies between degradation processes within a piecewise-deterministic Markov process (PDMP) modeling framework. Epistemic (subjective) uncertainty can arise due to the incomplete or imprecise knowledge about the degradation processes and the governing parameters: to take into account this, we describe the parameters of the PDMP model as fuzzy numbers. Then, we extend the finite-volume (FV) method to quantify the (fuzzy) reliability of the system. The proposed method is tested on one subsystem of the residual heat removal system (RHRS) of a nuclear power plant, and a comparison is offered with a Monte Carlo (MC) simulation solution: the results show that our method can be most efficient

    Development and characterisation of error functions in design

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    As simulation is increasingly used in product development, there is a need to better characterise the errors inherent in simulation techniques by comparing such techniques with evidence from experiment, test and inservice. This is necessary to allow judgement of the adequacy of simulations in place of physical tests and to identify situations where further data collection and experimentation need to be expended. This paper discusses a framework for uncertainty characterisation based on the management of design knowledge leading to the development and characterisation of error functions. A classication is devised in the framework to identify the most appropriate method for the representation of error, including probability theory, interval analysis and Fuzzy set theory. The development is demonstrated with two case studies to justify rationale of the framework. Such formal knowledge management of design simulation processes can facilitate utilisation of cumulated design knowledge as companies migrate from testing to simulation-based design

    Multi-expert operational risk management

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    Elicitation, assessment, and pooling of expert judgments using possibility theory

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    Un cadre holistique de la modélisation de la dégradation pour l’analyse de fiabilité et optimisation de la maintenance de systèmes de sécurité nucléaires

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    Components of nuclear safety systems are in general highly reliable, which leads to a difficulty in modeling their degradation and failure behaviors due to the limited amount of data available. Besides, the complexity of such modeling task is increased by the fact that these systems are often subject to multiple competing degradation processes and that these can be dependent under certain circumstances, and influenced by a number of external factors (e.g. temperature, stress, mechanical shocks, etc.). In this complicated problem setting, this PhD work aims to develop a holistic framework of models and computational methods for the reliability-based analysis and maintenance optimization of nuclear safety systems taking into account the available knowledge on the systems, degradation and failure behaviors, their dependencies, the external influencing factors and the associated uncertainties.The original scientific contributions of the work are: (1) For single components, we integrate random shocks into multi-state physics models for component reliability analysis, considering general dependencies between the degradation and two types of random shocks. (2) For multi-component systems (with a limited number of components):(a) a piecewise-deterministic Markov process modeling framework is developed to treat degradation dependency in a system whose degradation processes are modeled by physics-based models and multi-state models; (b) epistemic uncertainty due to incomplete or imprecise knowledge is considered and a finite-volume scheme is extended to assess the (fuzzy) system reliability; (c) the mean absolute deviation importance measures are extended for components with multiple dependent competing degradation processes and subject to maintenance; (d) the optimal maintenance policy considering epistemic uncertainty and degradation dependency is derived by combining finite-volume scheme, differential evolution and non-dominated sorting differential evolution; (e) the modeling framework of (a) is extended by including the impacts of random shocks on the dependent degradation processes.(3) For multi-component systems (with a large number of components), a reliability assessment method is proposed considering degradation dependency, by combining binary decision diagrams and Monte Carlo simulation to reduce computational costs.Composants de systèmes de sÝretÊ nuclÊaire sont en gÊnÊral très fiable, ce qui conduit à une difficultÊ de modÊliser leurs comportements de dÊgradation et d'Êchec en raison de la quantitÊ limitÊe de donnÊes disponibles. Par ailleurs, la complexitÊ de cette tâche de modÊlisation est augmentÊe par le fait que ces systèmes sont souvent l'objet de multiples processus concurrents de dÊgradation et que ceux-ci peut être dÊpendants dans certaines circonstances, et influencÊ par un certain nombre de facteurs externes (par exemple la tempÊrature, le stress, les chocs mÊcaniques, etc.).Dans ce cadre de problème compliquÊ, ce travail de thèse vise à dÊvelopper un cadre holistique de modèles et de mÊthodes de calcul pour l'analyse basÊe sur la fiabilitÊ et la maintenance d'optimisation des systèmes de sÝretÊ nuclÊaire en tenant compte des connaissances disponibles sur les systèmes, les comportements de dÊgradation et de dÊfaillance, de leurs dÊpendances, les facteurs influençant externes et les incertitudes associÊes.Les contributions scientifiques originales dans la thèse sont:(1) Pour les composants simples, nous intÊgrons des chocs alÊatoires dans les modèles de physique multi-Êtats pour l'analyse de la fiabilitÊ des composants qui envisagent dÊpendances gÊnÊrales entre la dÊgradation et de deux types de chocs alÊatoires.(2) Pour les systèmes multi-composants (avec un nombre limitÊ de composants):(a) un cadre de modÊlisation de processus de Markov dÊterministes par morceaux est dÊveloppÊ pour traiter la dÊpendance de dÊgradation dans un système dont les processus de dÊgradation sont modÊlisÊes par des modèles basÊs sur la physique et des modèles multi-Êtats; (b) l'incertitude ÊpistÊmique à cause de la connaissance incomplète ou imprÊcise est considÊrÊ et une mÊthode volumes finis est prolongÊe pour Êvaluer la fiabilitÊ (floue) du système; (c) les mesures d'importance de l'Êcart moyen absolu sont Êtendues pour les composants avec multiples processus concurrents dÊpendants de dÊgradation et soumis à l'entretien; (d) la politique optimale de maintenance compte tenu de l'incertitude ÊpistÊmique et la dÊpendance de dÊgradation est dÊrivÊ en combinant schÊma volumes finis, Êvolution diffÊrentielle et non-dominÊe de tri Êvolution diffÊrentielle; (e) le cadre de la modÊlisation de (a) est Êtendu en incluant les impacts des chocs alÊatoires sur les processus dÊpendants de dÊgradation.(3) Pour les systèmes multi-composants (avec un grand nombre de composants), une mÊthode d'Êvaluation de la fiabilitÊ est proposÊ considÊrant la dÊpendance dÊgradation en combinant des diagrammes de dÊcision binaires et simulation de Monte Carlo pour rÊduire le coÝt de calcul

    Um enfoque segundo a teoria de conjuntos difusos para a meta-anĂĄlise

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    Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico.A meta-anålise Ê um procedimento estatístico para a realização de sínteses de evidências. Como forma de inserir nos procedimentos padrþes a forma de raciocínio humano, tratou-se da variåvel com valores lingßísticos relevância como função das variåveis "p-value", tamanho da amostra e "effect size", construindo uma partição difusa do espaço amostral, ponderada por probabilidades subjetivas. Todas as variåveis foram tratadas como variåveis difusas. Uma aplicação para verificação da eficiência da aspirina no tratamento de pacientes pós-infarto foi realizada
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