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1 Dipartimento di Energia 2 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec LGI - Laboratoire Génie Industriel - EA 2606, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec

Abstract : Particle Filtering PF is a model-based, filtering technique, which has drawn the attention of the Prognostic and Health Management PHM community due to its applicability to nonlinear models with non-additive and non-Gaussian noise. When multiple physical models can describe the evolution of the degradation of a component, the PF approach can be based on Multiple Swarms MS of particles, each one evolving according to a different model, from which to select the most accurate a posteriori distribution. However, MS are highly computational demanding due to the large number of particles to simulate. In this work, to tackle the problem we have developed a PF approach based on the introduction of an augmented discrete state identifying the physical model describing the component evolution, which allows to detect the occurrence of abnormal conditions and identifying the degradation mechanism causing it. A crack growth degradation problem has been considered to prove the effectiveness of the proposed method in the detection of the crack initiation and the identification of the occurring degradation mechanism. The comparison of the obtained results with that of a literature MS method and of an empirical statistical test has shown that the proposed method provides both an early detection of the crack initiation, and an accurate and early identification of the degradation mechanism. A reduction of the computational cost is also achieved. 2

Keywords : Multi Model Systems Particle Filtering Fault Detection and Isolation





Autor: Michele Compare - Piero Baraldi - Pietro Turati - Enrico Zio -

Fuente: https://hal.archives-ouvertes.fr/



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