Araştırma Makalesi
Yıl 2018,
, 55 - 65, 30.03.2018
Öz
By using ambient vibration, a new approach based on
improvement and correction of system characteristic matrix in modal vibration
is provided. The result is that actual system characteristic matrices are
accurately made such that the error is minimized at great extent. This clearly
shows how the system parameters can be updated in a more reliable way. Firstly,
by approximation, the actual system characteristic matrices are determined
using the singular value decomposition of block Hankel matrix that is built
from response correlation matrix. Secondly, by black-box modeling
approximation, the input-output relation of the system through Kalman theory is
made in order to make the system characteristic matrices optimal definite.
Furthermore, by expressing Hankel matrix’s multiplicities from Eigen solution
of the system state matrix obtained in previous iteration, it is possible to
determine both the covariance of non-measurable process noise and measurement
noise matrixes which are present in the Riccati equation. This means that both
measurement and process covariance noises’ matrixes are indirectly built only
from measured out-put data. The repetition of iterations is done until the
error is sufficiently minimized. And then system modal parameters are extracted
from these obtained system characteristic matrices. This system is used for
modal update of the system in which modal parameters are applied directly and
iterative methods. The code supporting this algorithm can be interfaced with
the codes of the finite elements.
Anahtar Kelimeler
: Dynamic Parameters Ambient Vibration System Identification
Kaynakça
- Andersen P., Brincker R., Goursat M., Mevel L., (2007), Automated Modal Parameter Estimation for Operational Modal Analysis of Large Systems, Proceedings of The 2nd International Operational Modal Analysis Conference (IOMAC), Copenhagen, Denmark, ss.299-308. ARTeMIS, (2003), Structural Vibration Solution. Balmes, E., (1997), New results on the identification of normal modes from experimental complex modes, Mechanical Systems and Signal Processing, doi: 10.1006/mssp.1996.0058. Bendat J.S., (1998), Nonlinear System Techniques and Applications, Wiley-Interscience, New York, USA, 488 ss. Brownjohn J., Carden P., (2007), Reliability of Frequency and Damping Estimates from Free Vibration Response, Proceedings of The 2nd International Operational Modal Analysis Conference (IOMAC), Copenhagen, Denmark, ss.23-30. Caesar B., (1986), Update and Identification of Dynamic Mathematical Models, 4th International Modal Analysis Conference, Los Angeles, USA, ss.394-401. Chen G., (2001), FE Model Validation for Structural Dynamics, Ph.D. Thesis, University of London, London, UK. Cunha, A., Caetano, E., Magalhaes, F., Moutinho, C., (2005), From Input-Output to Output-Only Modal Identification of Civil Engineering Structures, First International Operational Modal Analysis Conference, Copenhagen, Denmark, ss.11-27. Dascotte E., Vanhonacker P., (1989), Development of an Automatic Mathematical Model Updating Program, Proceedings of the 7th International Modal Analysis Conference (IMAC), Las Vegas, Nevada, USA, ss. 596-602. Gawronski W., (2004), Advanced Structural Dynamics and Active Control of Structures, Springer-Verlag, New York, USA, 397 ss. Ge M., Lui E.M., (2005), Structural Damage Identification Using System Dynamic Properties, Computers and Structures, doi: 10.1016/j.compstruc.2005.05.002. Ibrahim S.R., (1977), Random Decrement Technique for Modal Identification of Structures, Journal of Spacecraft and Rockets, doi: 10.2514/3.57251. Johansson R., (1993), System Modeling and Identification, Prentice Hall, New Jersey, USA, 528 ss. Juang J.N., (1994), Applied System Identification, Prentice Hall, New Jersey, USA, 394 ss. Kasimzade A.A., Tuhta S., (2007), Particularities of Monitoring, Identification, Model Updating Hierarchy in Experimental Vibration Analysis of Structures, Experimental Vibration Analysis of Civil Engineering Structures (EVACES'07), Porto, Portugal, ss. 513-523. Kasimzade A.A., (2005), Finite Element Method: Foundation and Application to Earthquake Engineering, Istanbul, Beta Publication, Istanbul, Turkey, 827 ss. Kasimzade A.A., (2006), Coupling of the Control System and the System Identification Toolboxes with Application in Structural Dynamics, International Conference Control (ICC2006), Glasgow, Scotland, United Kingdom, ss. 59-64. Kowalczuk Z., Kozlowski J., (2000), Continuous-time Approaches to Identification of Continuous-time Systems, Automatica, doi: 10.1016/S0005-1098(00)00033-9. Labarre D., Grivel E., Najim M., Todini E., (2003), Two-Kalman Filter Approach for Unbiased AR Parameter Estimation from Noisy Observations, In Signal Processing Conference, Talence, France, ss. 633-636. Link M., (1993), Updating of Analytical Models-Procedures and Experience, In Proceedings of the Conference on Modern Practice in Stress and Vibration Analysis, University of Sheffield, UK, ss. 35-52. Ljung L., (1999), System Identification: Theory for the User, Prentice Hall, New Jersey, USA, 672 ss. Nelson A., (2000), Non-Linear Estimation and Modeling of Noisy Time-Series by Dual Kalman Filtering Methods, Ph.D. Thesis, Oregon Graduate Institute of Science & Technology, Oregon, USA. Kalman R. E., (1960), A new approach to linear filtering and prediction problems, Journal of Basic Engineering, doi:10.1115/1.3662552. Peeters B., (2000), System Identification and Damage Detection in Civil Engineering, Ph.D. Thesis, Katholieke Universiteit Leuven, Leuven, Belgium. Phan M.Q., Longman R.W., (2004), Extracting Mass, Stiffness, and Damping Matrices from Identified State-Space Models, AIAA-2004-5415, Providence, Rhode Island, USA, ss. 5415. Raol J.R., Madhuranath H., (1996), Neural Network Architectures for Parameter Estimation of Dynamical Systems, IEE Proceedings-Control Theory Applications, doi: 10.1049/ip-cta:19960338. Roeck G. D., (2003), The state‐of‐the‐art of damage detection by vibration monitoring: the SIMCES experience, Journal of Structural Control, doi: 10.1002/stc.20. Tuhta S., (2010), Non Destructive Evaluation of Structural Parameters, Ph.D. Thesis, Ondokuz Mayis University, Samsun, Turkey. Van Overschee P., De Moor B. L., (1996), Subspace Identification for Linear Systems: Theory-Implementation-Applications, Springer Science -Business Media, Netherlands, 272ss. Wei F.S., (1990), Structural Dynamic Model Improvement Using Vibration Test Data, AIAA Journal, 28(1), 175-177.
Yıl 2018,
, 55 - 65, 30.03.2018
Öz
Kaynakça
- Andersen P., Brincker R., Goursat M., Mevel L., (2007), Automated Modal Parameter Estimation for Operational Modal Analysis of Large Systems, Proceedings of The 2nd International Operational Modal Analysis Conference (IOMAC), Copenhagen, Denmark, ss.299-308. ARTeMIS, (2003), Structural Vibration Solution. Balmes, E., (1997), New results on the identification of normal modes from experimental complex modes, Mechanical Systems and Signal Processing, doi: 10.1006/mssp.1996.0058. Bendat J.S., (1998), Nonlinear System Techniques and Applications, Wiley-Interscience, New York, USA, 488 ss. Brownjohn J., Carden P., (2007), Reliability of Frequency and Damping Estimates from Free Vibration Response, Proceedings of The 2nd International Operational Modal Analysis Conference (IOMAC), Copenhagen, Denmark, ss.23-30. Caesar B., (1986), Update and Identification of Dynamic Mathematical Models, 4th International Modal Analysis Conference, Los Angeles, USA, ss.394-401. Chen G., (2001), FE Model Validation for Structural Dynamics, Ph.D. Thesis, University of London, London, UK. Cunha, A., Caetano, E., Magalhaes, F., Moutinho, C., (2005), From Input-Output to Output-Only Modal Identification of Civil Engineering Structures, First International Operational Modal Analysis Conference, Copenhagen, Denmark, ss.11-27. Dascotte E., Vanhonacker P., (1989), Development of an Automatic Mathematical Model Updating Program, Proceedings of the 7th International Modal Analysis Conference (IMAC), Las Vegas, Nevada, USA, ss. 596-602. Gawronski W., (2004), Advanced Structural Dynamics and Active Control of Structures, Springer-Verlag, New York, USA, 397 ss. Ge M., Lui E.M., (2005), Structural Damage Identification Using System Dynamic Properties, Computers and Structures, doi: 10.1016/j.compstruc.2005.05.002. Ibrahim S.R., (1977), Random Decrement Technique for Modal Identification of Structures, Journal of Spacecraft and Rockets, doi: 10.2514/3.57251. Johansson R., (1993), System Modeling and Identification, Prentice Hall, New Jersey, USA, 528 ss. Juang J.N., (1994), Applied System Identification, Prentice Hall, New Jersey, USA, 394 ss. Kasimzade A.A., Tuhta S., (2007), Particularities of Monitoring, Identification, Model Updating Hierarchy in Experimental Vibration Analysis of Structures, Experimental Vibration Analysis of Civil Engineering Structures (EVACES'07), Porto, Portugal, ss. 513-523. Kasimzade A.A., (2005), Finite Element Method: Foundation and Application to Earthquake Engineering, Istanbul, Beta Publication, Istanbul, Turkey, 827 ss. Kasimzade A.A., (2006), Coupling of the Control System and the System Identification Toolboxes with Application in Structural Dynamics, International Conference Control (ICC2006), Glasgow, Scotland, United Kingdom, ss. 59-64. Kowalczuk Z., Kozlowski J., (2000), Continuous-time Approaches to Identification of Continuous-time Systems, Automatica, doi: 10.1016/S0005-1098(00)00033-9. Labarre D., Grivel E., Najim M., Todini E., (2003), Two-Kalman Filter Approach for Unbiased AR Parameter Estimation from Noisy Observations, In Signal Processing Conference, Talence, France, ss. 633-636. Link M., (1993), Updating of Analytical Models-Procedures and Experience, In Proceedings of the Conference on Modern Practice in Stress and Vibration Analysis, University of Sheffield, UK, ss. 35-52. Ljung L., (1999), System Identification: Theory for the User, Prentice Hall, New Jersey, USA, 672 ss. Nelson A., (2000), Non-Linear Estimation and Modeling of Noisy Time-Series by Dual Kalman Filtering Methods, Ph.D. Thesis, Oregon Graduate Institute of Science & Technology, Oregon, USA. Kalman R. E., (1960), A new approach to linear filtering and prediction problems, Journal of Basic Engineering, doi:10.1115/1.3662552. Peeters B., (2000), System Identification and Damage Detection in Civil Engineering, Ph.D. Thesis, Katholieke Universiteit Leuven, Leuven, Belgium. Phan M.Q., Longman R.W., (2004), Extracting Mass, Stiffness, and Damping Matrices from Identified State-Space Models, AIAA-2004-5415, Providence, Rhode Island, USA, ss. 5415. Raol J.R., Madhuranath H., (1996), Neural Network Architectures for Parameter Estimation of Dynamical Systems, IEE Proceedings-Control Theory Applications, doi: 10.1049/ip-cta:19960338. Roeck G. D., (2003), The state‐of‐the‐art of damage detection by vibration monitoring: the SIMCES experience, Journal of Structural Control, doi: 10.1002/stc.20. Tuhta S., (2010), Non Destructive Evaluation of Structural Parameters, Ph.D. Thesis, Ondokuz Mayis University, Samsun, Turkey. Van Overschee P., De Moor B. L., (1996), Subspace Identification for Linear Systems: Theory-Implementation-Applications, Springer Science -Business Media, Netherlands, 272ss. Wei F.S., (1990), Structural Dynamic Model Improvement Using Vibration Test Data, AIAA Journal, 28(1), 175-177.
Toplam 1 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | İnşaat Mühendisliği |
| Bölüm | İnşaat Mühendisliği |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Mart 2018 |
| Gönderilme Tarihi | 18 Ekim 2017 |
| Yayımlandığı Sayı | Yıl 2018 |