Estimating the limit state exceeding probability of a deteriorating structure using the Kalman filter, extended Kalman filter, unscented Kalman filter and the sequential Monte Carlo simulation

This paper focuses on determining the limit state exceeding probability of a deteriorating model using optimal and sub-optimal Bayesian algorithms. Specifically the Kalman filter (for a linear system), extended Kalman filter, unscented Kalman filter and the sequential Monte Carlo simulation (for non...

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Bibliographic Details
Main Authors: Garciano, Lessandro Estelito O., Yoshida, Ikumasa
Format: text
Published: Animo Repository 2011
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/6033
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Institution: De La Salle University
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Summary:This paper focuses on determining the limit state exceeding probability of a deteriorating model using optimal and sub-optimal Bayesian algorithms. Specifically the Kalman filter (for a linear system), extended Kalman filter, unscented Kalman filter and the sequential Monte Carlo simulation (for non-linear systems) are used to approximate the present state of a deteriorating system given measurements tainted with noise of the system output. In addition to the comprehensive discussion of the theory, numerical implementation and comparison of the results through numerical examples are shown.