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Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

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
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Submitted on : Tuesday, January 26, 2016 - 12:00:26 PM
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2015_PREM_Interacting multiple...
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Michele Compare, Piero Baraldi, Pietro Turati, Enrico Zio. Interacting multiple-models, state augmented Particle Filtering for fault diagnostics. Probabilistic Engineering Mechanics, Elsevier, 2015, ⟨10.1016/j.probengmech.2015.01.001⟩. ⟨hal-01262136⟩



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