Seismic induced damage detection through parallel estimation of force and parameter using improved interacting Particle-Kalman filter

Subhamoy Sen 1 Antoine Crinière 1 Laurent Mevel 1, 2 Frédéric Cérou 3 Jean Dumoulin 2, 1
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
3 ASPI - Applications of interacting particle systems to statistics
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
Abstract : Standard filtering techniques for structural parameter estimation assume that the input force either is known exactly or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, a novel algorithm is proposed to estimate the force as additional state in parallel to the system parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, interacting Particle-Kalman filter [1] is employed targeting systems with correlated noise. Alongside a second filter is employed to estimate the seismic force acting on the structure. The proposal is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system. The estimation results confirm the applicability of the proposed algorithm.
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Communication dans un congrès
11th International Workshop on Structural Health Monitoring, Sep 2017, San Francisco, United States. 2017
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Soumis le : mercredi 20 septembre 2017 - 10:11:59
Dernière modification le : jeudi 11 janvier 2018 - 06:24:24

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Subhamoy Sen, Antoine Crinière, Laurent Mevel, Frédéric Cérou, Jean Dumoulin. Seismic induced damage detection through parallel estimation of force and parameter using improved interacting Particle-Kalman filter. 11th International Workshop on Structural Health Monitoring, Sep 2017, San Francisco, United States. 2017. 〈hal-01590713〉

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