Dual-Type Structural Response Reconstruction Based on Moving-Window Kalman Filter with Unknown Measurement Noise
Publication: Journal of Aerospace Engineering
Volume 32, Issue 4
Abstract
Various measurements are now available for structural health monitoring (SHM) due to the fast development of sensory systems. Utilization of multitype measurements including local and global information for SHM has typically outperformed that of solo type measurements. However, the limited number of sensors for measurements hampers the effectiveness of SHM. Thus, response reconstruction at the locations of interest in which sensors are unavailable with limited measurements has drawn significant research attention. The Kalman filter (KF) is a powerful tool to estimate optimally the unknown state vector of a structure that has numerous applications in civil engineering. One main concern for KF is that it requires good estimates of the noise covariance information, which is generally difficult to determine. Therefore, this paper investigates the dual-type responses reconstruction by using the moving-window Kalman filter (MWKF) with unknown measurement noise covariance (MNC). The weighted average of the MNC was first evaluated by utilizing the moving-window estimation technique. Then the dual-type of measurements including strains and displacements were fused together to reconstruct the structural responses at unmeasured locations. Numerical and experimental investigations were conducted to verify the effectiveness and feasibility of the MWKF in dual-type response reconstruction. The results indicate that the MNC can be well estimated and the reconstructed responses agree well with the real or measured responses.
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Acknowledgments
This research was fully supported by the grants from the National Natural Science Foundation of China (51608126) and the Natural Science Foundation of Fujian Province (2016J05124).
References
Eftekhar Azam, S., E. Chatzi, and C. Papadimitriou. 2015. “A dual Kalman filter approach for state estimation via output-only acceleration measurements.” Mech. Syst. Signal Process. 60–61: 866–886. https://doi.org/10.1016/j.ymssp.2015.02.001.
Fuad, A. G., Z. Anazida, A. R. Murad, and A. Ajith. 2017. “Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter.” Pervasive Mob. Comput. 40: 139–155. https://doi.org/10.1016/j.pmcj.2017.06.008.
He, J., X. Guan, and Y. Liu. 2012. “Structural response reconstruction based on empirical mode decomposition in time domain.” Mech. Syst. Signal Process. 28: 348–366. https://doi.org/10.1016/j.ymssp.2011.12.010.
Hoshiya, M., and E. Saito. 1984. “Structural identification by extended Kalman filter.” J. Eng. Mech. 110 (12): 1757–1770. https://doi.org/10.1061/(ASCE)0733-9399(1984)110:12(1757).
Iliopoulos, A., R. Shirzadeh, W. Weijtjens, P. Guillaume, D. V. Hemelrijck, and C. Devriendt. 2016. “A modal decomposition and expansion approach for prediction of dynamic response on a monopole offshore wind turbine using a limited number of vibration sensors.” Mech. Syst. Signal Process. 68–69: 84–104. https://doi.org/10.1016/j.ymssp.2015.07.016.
Jo, H., and B. F. Spencer. 2014. “Multi-metric displacement monitoring using model-based Kalman filter.” In Proc., 6th World Conf. on Structural Control and Monitoring. Barcelona, Spain: International Center for Numerical Methods in Engineering.
Kammer, D. C. 1997. “Estimation of structural response using remote sensor locations.” J. Guidance Control Dyn. 20 (3): 501–508. https://doi.org/10.2514/2.4069.
Lai, T., T. H. Yi, and H. N. Li. 2017. “Wavelet-Galerkin method for reconstruction of structural dynamic responses.” Adv. Struct. Eng. 20 (8): 1174–1184. https://doi.org/10.1177/1369433216677754.
Lai, Z., Y. Lei, S. Zhu, Y. L. Xu, X. H. Zhang, and S. Krishnaswamy. 2016. “Moving-window extended Kalman filter for structural damage detection with unknown process and measurement noises.” Measurement 88: 428–440. https://doi.org/10.1016/j.measurement.2016.04.016.
Lei, Y., F. Chen, and H. Zhou. 2015. “An algorithm based on two-step Kalman filter for intelligent structural damage detection.” Struct. Control Health Monit. 22 (4): 694–706. https://doi.org/10.1002/stc.1712.
Li, J., and H. Hao. 2014. “Substructure damage identification based on wavelet-domain response reconstruction.” Struct. Health Monit. 13 (4): 389–405. https://doi.org/10.1177/1475921714532991.
Li, J., and S. S. Law. 2011. “Substructural response reconstruction in wavelet domain.” J. Appl. Mech. ASME 78 (4): 041010. https://doi.org/10.1115/1.4003738.
Li, J., S. S. Law, and Y. Ding. 2013. “Damage detection of a substructure based on response reconstruction in frequency domain.” Key Eng. Mater. 569–570: 823–830. https://doi.org/10.4028/www.scientific.net/KEM.569-570.823.
Lu, W., J. Teng, Y. L. Xu, and Z. Q. Su. 2013. “Identification of damage in dome-like structures using hybrid sensor measurements and artificial neural networks.” Smart Mater. Struct. 22 (10): 105–114. https://doi.org/10.1088/0964-1726/22/10/105014.
Mace, B. R., and C. R. Halkyard. 2000. “Time domain estimation of response and intensity in beams using wave decomposition and reconstruction.” J. Sound Vib. 230 (3): 561–589. https://doi.org/10.1006/jsvi.1999.2630.
Matthew, C. B., and B. Karol. 2017. “Extending the Kalman filter for structured identification of linear and nonlinear systems.” Int. J. Modell. Identif. Control 27 (2): 114–124. https://doi.org/10.1504/IJMIC.2017.082952.
Mohamed, A. H., and K. P. Schwarz. 1999. “Adaptive Kalman filtering for INS/GPS.” J. Geod. 73 (4): 193–203. https://doi.org/10.1007/s001900050236.
Niu, Y., C. P. Firtzen, H. Jung, I. Buethe, Y. Q. Ni, and Y. W. Wang. 2015. “Online simultaneous reconstruction of wind load and structural responses—Theory and application to Canton Tower.” Comput. Aided Civ. Infrastruct. Eng. 30 (8): 666–681. https://doi.org/10.1111/mice.12134.
Papadimitriou, C., C. P. Fritzen, P. Kraemer, and E. Ntotsios. 2011. “Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering.” Struct. Control Health Monit. 18 (5): 554–573. https://doi.org/10.1002/stc.395.
Petersen, Ø. W., O. Øiseth, T. S. Nord, and E. Lourens. 2018. “Estimatin of the full-field dynamic response of a floating bridge using Kalman-type filtering algorithms.” Mech. Syst. Signal Process. 107: 12–28. https://doi.org/10.1016/j.ymssp.2018.01.022.
Rodrigo, A., T. N. Luan, and N. Tamara. 2016. “Finite element model updating using simulated annealing hybridized with unscented Kalman filter.” Comput. Struct. 177: 176–191. https://doi.org/10.1016/j.compstruc.2016.09.001.
Sim, S. H., and B. F. Spencer Jr. 2007. “Multi-scale sensing for structural health monitoring.” In Proc., World Forum on Smart Materials and Smart Structures Technology. Abingdon, UK: Taylor & Francis.
Wan, Z. M., S. D. Li, Q. B. Huang, and T. Wang. 2014. “Structural response reconstruction based on the modal superposition method in the presence of closely spaced modes.” Mech. Syst. Signal Process. 42 (1–2): 14–30. https://doi.org/10.1016/j.ymssp.2013.07.007.
Zhang, C. D., and Y. L. Xu. 2016. “Structural damage identification via multi-type sensors and response reconstruction.” Struct. Health Monit. 15 (6): 715–729. https://doi.org/10.1177/1475921716659787.
Zhang, C. D., and Y. L. Xu. 2017. “Multi-level damage identification with response reconstruction.” Mech. Syst. Signal Process. 95: 42–57. https://doi.org/10.1016/j.ymssp.2017.03.029.
Zhang, X. H., Y. L. Xu, S. Zhan, and S. Zhu. 2014. “Dual-type sensor placement for multi-scale response reconstruction.” Mechatron. Int. J. 24 (4): 376–384. https://doi.org/10.1016/j.mechatronics.2013.05.007.
Zhang, X. H., S. Zhu, Y. L. Xu, and X. J. Hong. 2011. “Integrated optimal placement of displacement transducers and strain gauges for better estimation of structural response.” Int. J. Struct. Stab. Dyn. 11 (3): 581–602. https://doi.org/10.1142/S0219455411004221.
Zheng, B., P. Fu, B. Li, and X. Yuan. 2018. “A robust adaptive unscented Kalman filter for nonlinear estimation with uncertain noise covariance.” Sensors 18 (3): 808. https://doi.org/10.3390/s18030808.
Zhi, L. H., P. Yu, J. W. Tu, B. Chen, and Y. G. Li. 2017. “A Kalman filter based algorithm for wind load estimation on high-rise buildings.” Struct. Eng. Mech. 64 (4): 449–459. https://doi.org/10.12989/sem.2017.64.4.449.
Zhu, S., X. H. Zhang, S. Zhan, and Y. L. Xu. 2013. “Multi-type sensor placement for multi-scale response reconstruction.” Adv. Struct. Eng. 16 (10): 1779–1797. https://doi.org/10.1260/1369-4332.16.10.1779.
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Information
Published In
Journal of Aerospace Engineering
Volume 32 • Issue 4 • July 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Aug 14, 2018
Accepted: Dec 3, 2018
Published online: Mar 28, 2019
Published in print: Jul 1, 2019
Discussion open until: Aug 28, 2019
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