Abstract
In this study, a novel dual implementation of the Kalman filter is proposed for simultaneous estimation of the states and input of structures via acceleration measurements. In practice, the uncertainties stemming from the absence of information on the input force, model inaccuracy and measurement errors render the state estimation a challenging task and the research to achieve a robust solution is still in progress. Via the use of numerical simulation, it was shown that the proposed method outperforms the existing techniques in terms of robustness and accuracy of displacement and velocity estimations [8]. The efficacy of the proposed method is validated using the data obtained from a shake table experiment on a laboratory test structure. The measured accelerations of the floors of the structure are fed into the filter, and the estimated time histories of the displacement estimates are cross-compared to the true time histories obtained from the displacement sensors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Papadimitriou C, Fritzen CP, Kraemer P, Ntotsios E (2011) Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. Struct Control Health Monit 18:554–573
Bernal D, Ussia A (2015) Sequential deconvolution input reconstruction. Mech Syst Signal Process 50–51:41–55
Gillijns S, De Moor B (2007) Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica 43:934–937
Lourens E, Papadimitriou C, Gillijns S, Reynders E, De Roeck G, Lombaert G (2012) Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors. Mech Syst Signal Process 29:310–327
Chatzi E, Fuggini C (2014) Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman filter. Special Volume of Smart Structures and Systems on Errors / uncertainties in sensors for Structural Health Monitoring, (in press)
Lourens E, Reynders E, De Roeck G, Degrande G, Lombaert G (2012) An augmented Kalman filter for force identification in structural dynamics. Mech Syst Signal Process 27:446–460
Naets F, Cuadrado J, Desmet W (2015) Stable force identification in structural dynamics using Kalman filtering and dummy-measurements. Mech Syst Signal Process 50–51:235–248
Eftekhar Azam S, Papadimitriou C, Chatzi EN (2014) Recursive Bayesian filtering for displacement estimation via output-only vibration measurements. In: Proceedings of the 2014 world congress on advances in civil, environmental, and materials research, presented at the ACEM’14, Busan
Wenzel TA, Burnham K, Blundell M, Williams R (2006) Dual extended Kalman filter for vehicle state and parameter estimation. Veh Syst Dyn 44:153–171
Mariani S, Corigliano A (2005) Impact induced composite delamination: state and parameter identification via joint and dual extended Kalman filters. Comput Methods Appl Mech Eng 194:5242–5272
Eftekhar Azam S (2014) Online damage detection in structural systems. Springer, Cham, Switzerland
Van Overschee P, De Moor B (1994) N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30:75–93
Verboven P (2002) Frequency domain system identification for modal analysis. Ph.D. thesis, Department of Mechanical Engineering, Vrije Universiteit, Brussels
Cauberghe B (2004) Applied frequency-domain system identification in the field of experimental and operational modal analysis. Ph.D. thesis, Department of Mechanical Engineering, Vrije Universiteit, Brussels
Heylen W, Lammens S, Sas P (1997) Modal analysis theory and testing. Department of Mechanical Engineering, Katholieke Universiteit, Leuven
Chatzis MN, Chatzi EN, Smyth AW (2014) An experimental validation of time domain system identification methods with fusion of heterogeneous data. Earthq Eng Struct Dyn, doi: 10.1002/eqe.2528
Acknowledgement
This research has been implemented under the “ARISTEIA” Action of the “Operational Programme Education and Lifelong Learning” and was co-funded by the European Social Fund (ESF) and Greek National Resources. The authors would also like to thank Associate Professor Manolis Chatzis for his help in the acquisition and processing of the experimental data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Eftekhar Azam, S., Chatzi, E., Papadimitriou, C., Smyth, A. (2015). Experimental Validation of the Dual Kalman Filter for Online and Real-Time State and Input Estimation. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15224-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-15224-0_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15223-3
Online ISBN: 978-3-319-15224-0
eBook Packages: EngineeringEngineering (R0)