Two-Stage Covariance-Based Multisensing Damage Detection Method
Publication: Journal of Engineering Mechanics
Volume 143, Issue 3
Abstract
Different types of sensors in a structural health monitoring (SHM) system installed in a structure enable various types of structural responses to be measured. However, their distinct properties and limitations considerably complicate multisensing structural condition assessment. As a result, the information from these sensors is often used separately, and the potential advantage of multisensing information has not been used effectively. This paper first proposes a covariance-based multisensing (CBMS) damage detection method in the time domain in terms of a CBMS vector as a new damage index and a sensitivity study for damage detection. The proposed method has the merit of assimilating heterogeneous data and reducing the adverse effect of measurement noise. The CBMS damage detection method is then used in two stages for detecting damage location and severity consecutively. Numerical studies are finally performed to investigate the feasibility and accuracy of the proposed framework using an overhanging beam with two damage scenarios. The results show that the two-stage CBMS damage detection method improves the accuracy of damage detection and that the proposed method can be effectively used to combine multisensing information for better damage detection.
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Acknowledgments
The authors wish to acknowledge the financial support from the Research Grants Council of Hong Kong (PolyU 5289/12E). Any opinions and conclusions presented in this paper are entirely those of the authors.
References
Bendat, J. S., and Piersol, A. G. (1993). Engineering applications of correlation and spectral analysis, Wiley, New York.
Cattarius, J., and Inman, D. J. (1997). “Time domain analysis for damage detection in smart structrues.” Mech. Syst. Sig. Process., 11(3), 409–423.
Cawley, P., and Adams, R. D. (1979). “The location of defects in structures from measurements of natural frequencies.” J. Strain Anal. Eng. Des., 14(2), 49–57.
Chan, W. S., Xu, Y. L., Ding, X. L., and Dai, W. J. (2006). “An integrated GPS-accelerometer data processing technique for structural deformation monitoring.” J. Geod., 80(12), 705–719.
Chance, J., Tomlinson, G. R., and Worden, K. (1994). “A simplified approach to the numerical and experimental modeling of the dynamics of a cracked beam.” Proc., 12th Int. Modal Analysis Conf., Vol. 2251, CiteseerX, Pennsylvania, 778–785.
Choi, S., and Stubbs, N. (2004). “Damage identification in structures using the time-domain response.” J. Sound Vib., 275(3–5), 577–590.
Doebling, S. W., Farrar, C. R., and Prime, M. B. (1998). “A summary review of vibration-based damage identification methods.” Shock Vib. Dig., 30(2), 91–105.
Donoho, D. L. (2006). “For most large underdetermined systems of linear equations the minimal.” Commun. Pure Appl. Math., 59(6), 797–829.
Farrar, C. R., et al. (1994). “Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande.” Los Alamos National Lab, Los Alamos, NM.
Hansen, P. C., and O’Leary, D. P. (1993). “The use of the L-curve in the regularization of discrete ill-posed problems.” SIAM J. Sci. Comput., 14(6), 1487–1503.
Kammer, D. C. (1991). “Sensor placement for on-orbit modal identification and correlation of large space structures.” J. Guide Control Dyn., 14(2), 251–259.
Kohavi, R. (1995). “A study of cross-validation and bootstrap for accuracy estimation and model selection.” IJCAI’95 Proc., 14th Int. Joint Conf. on Artificial Intelligence, Vol. 2, Canadian Society for Computational Studies of Intelligence (CSCSI), Canada, 1137–1145.
Kwon, Y. W., and Bang, H. (2000). The finite element method using MATLAB, CRC Press, Boca Raton.
Law, S. S., Li, X. Y., Zhu, X. Q., and Chan, S. L. (2005). “Structural damage detection from wavelet packet sensitivity.” Eng. Struct., 27(9), 1339–1348.
Law, S. S., Lin, J. F., and Li, X. Y. (2012). “Structural condition assessment from white noise excitation and covariance of covariance matrix.” AIAA J., 50(7), 1503–1512.
Lee, E. T., Rahmatalla, S., and Eun, H. C. (2013). “Damage detection by mixed measurements using accelerometers and strain gages.” Smart Mater. Struct., 22(7), 075014.
Li, X. Y., and Law, S. S. (2010a). “Adaptive Tikhonov regularization for damage detection based on nonlinear model updating.” Mech. Syst. Sig. Process., 24(6), 1646–1664.
Li, X. Y., and Law, S. S. (2010b). “Matrix of the covariance of covariance of acceleration responses for damage detection from ambient vibration measurements.” Mech. Syst. Sig. Process., 24(4), 945–956.
Logan, D. L. (2002). A first course in the finite element method, Brooks/Cole, Pacific Grove, CA.
Lu, Z. R., and Law, S. S. (2007). “Features of dynamic response sensitivity and its application in damage detection.” J. Sound Vib., 303(1–2), 305–329.
Sim, S. H., Spencer, B. F., and Nagayama, T. (2011). “Multimetric sensing for structural damage detection.” J. Eng. Mech., 22–30.
Sohn, H., Farrar, C. R., Hemez, F., and Czarnecki, J. (2002). “A review of structure health monitoring literature 1996–2001.” Los Alamos National Lab, Los Alamos, NM.
Studer, M., and Peters, K. (2004). “Multi-scale sensing for damage identification.” Smart Mater. Struct., 13(2), 283–294.
Sun, Z., and Chang, C. C. (2006). “Covariance-driven wavelet technique for structural damage assessment.” Smart Struct. Syst., 2(2), 127–140.
Sung, S. H., Park, J. W., Nagayama, T., and Jung, H. J. (2014). “A multi-scale sensing and diagnosis system combining accelerometers and gyroscopes for bridge health monitoring.” Smart Mater. Struct., 23(1), 015005.
Xiang, J. W., and Liang, M. (2012). “A two-step approach to multi-damage detection for plate structures.” Eng. Fract. Mech., 91, 73–86.
Xu, Y. L., Zhang, J., Li, J. C., and Xia, Y. (2009). “Experimental investigation on statistical moment-based structural damage detection method.” Struct. Health Monit., 8(6), 555–571.
Yao, L., Sethares, W. A., and Kammer, D. C. (1992). “Sensor placement for on-Orbit modal identification of large space structure via a genetic algorithm.” IEEE Int. Conf. on Systems Engineering, Dept. of Electrical Engineering, Wright State Univ., Fairborn, OH, 332–335.
Yu, L., and Lin, J. C. (2015a). “Cloud computing-based time series analysis for structural damage detection.” J. Eng. Mech., C4015002.
Yu, L., and Zhu, J. H. (2015b). “Nonlinear damage detection using higher statistical moments of structural responses.” Struct. Eng. Mech., 54(2), 221–237.
Zhang, J., Xu, Y. L., Xia, Y., and Li, J. (2008). “A new statistical moment-based structural damage detection method.” Struct. Eng. Mech., 30(4), 445–466.
Zhang, X. H., Zhu, S., Xu, Y. L., and Homg, X. J. (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.
Zou, H., and Hastie, T. (2005). “Regularization and variable selection via the elastic net.” J. Roy. Stat. Soc. B Stat. Meth., 67(2), 301–320.
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Published In
Journal of Engineering Mechanics
Volume 143 • Issue 3 • March 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Apr 4, 2015
Accepted: Oct 30, 2015
Published online: Feb 26, 2016
Discussion open until: Jul 26, 2016
Published in print: Mar 1, 2017
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