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- W2613567942 abstract "A major concern for engineers and managers nowadays is to make high quality products and highly reliable systems. In this context, reliability analysis and failure prediction, besides of efficient maintenance decision-making are strongly required. Deterioration modeling and analysis is a fundamental step for the understanding and the anticipation of system behavior. Consider a functional system operating in unstable conditions or environment where the deterioration level is not observable and could not be determined by direct measures. For this system a set of measurable health indicator that indirectly reflects the system working conditions and deterioration level can be defined and examined. Considering these indicators, the development of a mathematical model describing the system behavior is required.In this thesis, we consider a set of non-monotone indicators evolving in a dynamic environment. Taking into account the major features of the data evolution as well as the impact of dynamic environment consequences and potential shocks, stochastic models based on Wiener and jump processes are proposed for these indicators. Each model is calibrated and tested, and their limits are discussed. A decision-making approach for preventive maintenance strategies is then proposed. In this approach, knowing the RUL of the system, a simulation-based real options analysis is used in order to determine the best date to maintain. Considering a case study of a wind turbine with PHM structure, the decision optimization approach is described" @default.
- W2613567942 created "2017-05-19" @default.
- W2613567942 creator A5042772837 @default.
- W2613567942 date "2016-06-21" @default.
- W2613567942 modified "2023-09-27" @default.
- W2613567942 title "Modélisation de la dégradation, maintenance conditionnelle et pronostic : usage des processus de diffusion" @default.
- W2613567942 hasPublicationYear "2016" @default.
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