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Open AccessFeature PaperArticle

Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

1
Center for Risk and Reliability, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
2
B. John Garrick Institute for the Risk Sciences, University of California (UCLA), Los Angeles, CA 90095, USA
3
Department of Mechanical Engineering, University of Chile, Santiago 8370448, Chile
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(2), 100; https://doi.org/10.3390/e20020100
Received: 14 November 2017 / Revised: 18 January 2018 / Accepted: 24 January 2018 / Published: 31 January 2018
(This article belongs to the Special Issue Entropy for Characterization of Uncertainty in Risk and Reliability)
A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback–Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials. View Full-Text
Keywords: fully adaptive particle filtering; cross entropy method; relative entropy; Kullback–Leibler divergence; adaptive measurement model; diagnosis and prognosis; composite degradation fully adaptive particle filtering; cross entropy method; relative entropy; Kullback–Leibler divergence; adaptive measurement model; diagnosis and prognosis; composite degradation
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MDPI and ACS Style

Rabiei, E.; Droguett, E.L.; Modarres, M. Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis. Entropy 2018, 20, 100.

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