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Vladik Kreinovich, The University of Texas at El PasoFollow Christelle Jacob, Institut Supérieur de l’Aéronautique et de l’Espace (ISAE) Didier Dubois, Institut de Recherche en Informatique de Toulouse (IRIT) Janette Cardoso, Institut Supérieur de l’Aéronautique et de l’Espace (ISAE) Martine Ceberio, The University of Texas at El PasoFollow Ildar Batyrshin, Instituto Mexicano de Petróleo
6-2011
Technical Report: UTEP-CS-11-30b
Published in: I. Batyrshin and G. Sidorov (eds.), Proceedings of the 10th Mexican International Conference on Artificial Intelligence MICAI'2011, Puebla, Mexico, November 26 - December 4, 2011, Springer Lecture Notes in Artificial Intelligence, Vol. 7095, pp. 70-81.
In many real-life applications (e.g., in aircraft maintenance), we need to estimate the probability of failure of a complex system (such as an aircraft as a whole or one of its subsystems). Complex systems are usually built with redundancy allowing them to withstand the failure of a small number of components. In this paper, we assume that we know the structure of the system, and, as a result, for each possible set of failed components, we can tell whether this set will lead to a system failure. For each component A, we know the probability P(A) of its failure with some uncertainty: e.g., we know the lower and upper bounds for this probability. Usually, it is assumed that failures of different components are independent events. Our objective is to use all this information to estimate the probability of failure of the entire the complex system. In this paper, we describe a new efficient method for such estimation based on Cauchy deviates.
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Technical Report: UTEP-CS-11-30b
Published in: I. Batyrshin and G. Sidorov (eds.), Proceedings of the 10th Mexican International Conference on Artificial Intelligence MICAI'2011, Puebla, Mexico, November 26 - December 4, 2011, Springer Lecture Notes in Artificial Intelligence, Vol. 7095, pp. 70-81.