Adaptive Kalman filters for nonlinear finite element model updating
Datum
2020Language
en
Schlagwort
Zusammenfassung
This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance. © 2020 Elsevier Ltd
Collections
Verwandte Dokumente
Anzeige der Dokumente mit ähnlichem Titel, Autor, Urheber und Thema.
-
Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
Nabiyan M.-S., Ebrahimian H., Moaveni B., Papadimitriou C. (2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
Nonlinear Model Updating Using Recursive and Batch Bayesian Methods
Song M., Astroza R., Ebrahimian H., Moaveni B., Papadimitriou C. (2020)This paper studies the performance of recursive and batch Bayesian methods for nonlinear model updating. Unscented Kalman filter (UKF) is selected to represent the recursive Bayesian method, and two UKF approaches are ... -
Overview of Diesel particulate filter systems sizing approaches
Stamatellou A.-M., Stamatelos A. (2017)Although application of Diesel particulate filters in modern automotive Diesel engines is commonplace, their introduction to large Diesels as locomotive or marine engines is moving at a slower pace. One important reason ...