Damage Detection of Seismically Excited Buildings Based on Prediction Errors
Publication: Journal of Aerospace Engineering
Volume 31, Issue 4
Abstract
Buildings may suffer serious damage when subjected to extreme loadings such as strong winds and earthquakes. In seismic events, the error time histories between measured and estimated responses should contain the information of the structural deterioration, i.e., the locations, levels, and time of occurrences. Therefore, this study presents a new damage detection method based on prediction errors using a bank of Kalman estimators. A representative model of a building was derived from a frequency-domain multiinput, multioutput system identification method under ambient vibration prior to earthquakes. This model was then converted into a bank of estimators that calculate estimation errors. Damage was interpreted by statistical indices from these errors and allowed determining the occurrence, levels, and locations of damage. A numerical example is presented to demonstrate the proposed damage detection method as well as to exhibit the damage detection performance. A series of experimental tests were carried out with this damage detection method implemented in various scenarios. The experimental verification shows that this proposed method is quite effective for seismic damage detection.
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Acknowledgments
This research was supported by the Ministry of Science and Technology in Taiwan under Grant Nos. MOST 104-2218-E-002-036 and MOST 105-3011-F-009-003.
References
Alkahe, J., Oshman, Y., and Rand, O. (2002). “Adaptive estimation methodology for helicopter blade structural damage detection.” J. Guidance Control Dyn., 25(6), 1049–1057.
Chang, C. M., and Spencer, B. F., Jr. (2013). “Hybrid system identification for high-performance structural control.” Eng. Struct., 56, 443–456.
Doebling, S. W., Farrar, C. R., and Prime, M. B. (1998). “A summary review of vibration-based damage identification methods.” Shock Vibr. Digest, 30(2), 91–105.
Feldman, M., and Seibold, S. (1999). “Damage diagnosis of rotors: Application of Hilbert transform and multihypothesis testing.” J. Vibr. Control, 5(3), 421–442.
Fritzen, C. P., Seibold, S., and Buchen, D. (1995). “Application of filter techniques for damage detection in linear and nonlinear mechanical structures.” Proc., 13th Int. Modal Analysis Conf., Society for Experimental Mechanics, Bethel, CT.
Haber, R., and Unbehauen, H. (1990). “Structure identification of nonlinear dynamic systems—A survey on input/output approaches.” Automatica, 26(4), 651–677.
Hanlon, P. D., and Maybeck, S. M. (2000). “Multiple-model adaptive estimation using a residual correlation Kalman filter bank.” IEEE Trans. Aerosp. Electron. Syst., 36(2), 393–406.
Kim, S. B., Spencer, B. F., Jr., and Yun, C. B. (2005). “Frequency domain identification of multi-input, multi-output systems considering physical relationships between measured variables.” J. Eng. Mech., 461–472.
Kobayashi, T., and Simon, D. L. (2005). “Evaluation of an enhanced bank of Kalman filters for aircraft engine sensor fault diagnostics.” J. Eng. Gas Turbines Power, 127(3), 497–504.
Lewis, F. (1986). Optimal estimation: With an introduction to stochastic control theory, Wiley, New York.
Lim, J. K., and Park, C. G. (2014). “Satellite fault detection and isolation scheme with modified adaptive fading EKF.” J. Electr. Eng. Technol., 9(4), 1401–1410.
Liu, G., Mao, Z., Todd, M., and Huang, Z. (2014). “Localization of nonlinear damage using state-space-based predictions under stochastic excitation.” Smart Mater. Struct., 23(2), 025036.
Lynch, J. P., Sundararajan, A., Law, K. H., Kuremidjian, A. S., and Carryer, E. (2004). “Embedding damage detection algorithm in a wireless sensing unit for operational power efficiency.” Smart Mater. Struct., 13(4), 800–810.
Masri, S. F., Smyth, A. W., Chassiakos, A. G., Caughey, T. K., and Hunter, N. F. (2000). “Application of neural networks for detection of changes in nonlinear systems.” J. Eng. Mech., 666–676.
Mellinger, P., Dhler, M., and Mevel, L. (2016). “Variance estimation of modal parameters from output-only and input/output subspace-based system identification.” J. Sound Vibr., 379(2016), 1–27.
Merrill, W. C., Delaat, J. C., and Bruton, W. M. (1998). “Advanced detection isolation, and accommodation of sensor failures—Real-time evaluation.” J. Guid. Control Dyn., 11(6), 517–526.
Pandey, A. K., and Biswas, M. (1994). “Damage detection in structures using changes in flexibility.” J. Sound Vibr., 169(1), 3–17.
Pebrianti, D., Samad, R., Mustafa, M., Abdullah, N. R. H., and Bayuaji, L. (2016). “Bank of Kalman filters for fault detection in quadrotor MAV.” ARPN J. Eng. Appl. Sci., 11(10), 6668–6674.
Peeters, B., and Roeck, G. D. (1999). “Reference-based stochastic subspace identification for output-only modal analysis.” Mech. Syst. Signal Process., 13(6), 855–878.
Rytter, A. (1993). “Vibration based inspection of civil engineering structures.” Ph.D. dissertation, Aalborg Univ., Aalborg, Denmark.
Saravanakumar, R., Monimozhi, M., Kothari, D. P., and Tejenosh, M. (2014). “Simulation of sensor fault diagnosis for wind turbine generators DFIG and PMSM using Kalman filter.” Energy Procedia, 54(2014), 494–505.
Verhaegen, M. (1994). “Identification of the deterministic part of MIMO state space models given in innovations form from input-output data.” Automatica, 30(1), 61–74.
Yan, Y. J., Cheng, L., Wu, Z. Y., and Yam, L. H. (2007). “Development in vibration-based structural damage detection technique.” Mech. Syst. Signal Process., 21(5), 2198–2211.
Yang, J. N., Xia, Y., and Loh, C. H. (2014). “Damage detection of hysteretic structures with pinching effect.” J. Eng. Mech., 462–472.
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©2018 American Society of Civil Engineers.
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Received: Jun 5, 2017
Accepted: Oct 5, 2017
Published online: Apr 10, 2018
Published in print: Jul 1, 2018
Discussion open until: Sep 10, 2018
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