TECHNICAL PAPERS
Sep 1, 2007

Structural Health Monitoring and Damage Assessment Using Frequency Response Correlation Criteria

Publication: Journal of Engineering Mechanics
Volume 133, Issue 9

Abstract

Two frequency response correlation criteria, namely the global shape correlation (GSC) function and the global amplitude correlation (GAC) function, are established tools to quantify the correlation between predictions from a finite-element (FE) model and measured data for the purposes of FE model validation and updating. This paper extends the application of these two correlation criteria to structural health monitoring and damage detection. In addition, window-averaged versions of the GSC and GAC, namely WAIGSC and WAIGAC, are defined as effective damage indicators to quantify the change in structural response. An integrated method of structural health monitoring and damage assessment, based on the correlation functions and radial basis function neural networks, is proposed and the technique is applied to a bookshelf structure with 24 measured responses. The undamaged and damaged states, single and multiple damage locations, as well as damage levels, were successfully identified in all cases studied. The ability of the proposed method to cope with incomplete measurements is also discussed.

Get full access to this article

View all available purchase options and get full access to this article.

References

Allemang, R. J., and Brown, D. J. (1982). “A correlation coefficient for modal vector analysis.” 1st Int. Modal Analysis Conf., Orlando, Fla., 110–116.
Avitable, P. (1998). “Correlation Considerations—Parts 1–5.” 16th Int. Modal Analysis Conf., Santa Barbara, Calif.
Baker, M. (1996). “Review of test/analysis correlation methods and criteria for validation of finite element models for dynamic analysis.” 14th Int. Modal Analysis Conf., Dearborn, Mich., 985–991.
Bi, T., Yan, Z., Wen, F., Ni, Y., Shen, C. M., Wu, F. F., and Yang, Q. (2002). “On-line fault section estimation in power systems with radial basis function neural network.” Int. J. Electr. Power Energy Syst., 24(4), 321–328.
Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, New York.
Broomhead, D., and Lowe, D. (1988). “Multivariable functional interpolation and adaptive neural networks.” Complex Syst., 2(3), 321–355.
Chen, S., Cowan, C. F. N., and Grant, P. M. (1991). “Orthogonal least squares learning algorithm for radial basis function networks.” IEEE Trans. Neural Netw., 2(2), 302–309.
Darken, C. J., and Moody, T. J. (1990). “Fast adaptive k-mean clustering: Some empirical results.” Proc. Int. Joint Conf. on Neural Networks, IEEE, San Diego.
Doebling, S. W., Farrar, C. R., Prime, M. B., and Shevitz, D. W. (1996). “Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review.” Rep. No. LA-13070-MS, Los Alamos Laboratory, Los Alamos, N.M.
Ewins, D. J. (2000). Modal testing: Theory, practice, and application, 2nd Ed., Research Studies Press, Baldock, Hertfordshire, U.K.
Farrar, C. R., Duffey, T. A., Doebling, S. W., and Nix, D. A. (1999). “A statistical pattern recognition paradigm for vibration-based structural health monitoring.” 2nd Int. Workshop on Structural Health Monitoring, Lancaster, Pa., 764–773.
Fukunaga, K. (1990). Introduction to statistical pattern recognition, 2nd Ed., Academic, San Diego.
Heylen, W., and Avitable, P. (1998). “Correlation considerations—Part 5.” Proc., 16th Int. Modal Analysis Conf., Santa Barbara, Calif., 207–214.
Heylen, W., and Lammens, S. (1996). “FRAC: A consistent way of comparing frequency response functions.” Proc., Conf. on Identification in Engineering Systems, Swansea, U.K.
Hwang, Y. S., and Bang, S. Y. (1997). “An efficient method to construct a radial basis function neural network classifier.” Neural Networks, 10(8), 1495–1503.
Kinsella, J. A. (1991). “Comparison and evaluation of variants of the conjugate gradient method for efficient learning in feed-forward neural networks with backward error propagation.” Network, 3(2), 27–35.
Lippmann, R. P. (1987). “An introduction to computing with neural nets.” IEEE ASSP Mag., 4(2), 4–22.
Los Alamos National Laboratory. (2002). “Structural health monitoring website.” ⟨http://www.lanl.gov/projects/damage_id/index.htm⟩.
Markham, I. S., and Ragsdale, C. T. (1995). “Combining neural networks and statistical predictions to solve the classification problem in discriminant analysis.” Decision Sci., 26(2), 229–242.
Messina, A., Williams, E. J., and Contursi, T. (1998). “Structural damage detection by a sensitivity and statistical-based method.” J. Sound Vib., 216(5), 791–808.
Moller, M. F. (1993). “A scaled conjugate gradient algorithm for fast supervised learning.” Neural Networks, 6(4), 525–533.
Moody, J., and Darken, C. J. (1989). “Fast learning in networks of locally tuned processing units.” Neural Comput., 1(2), 281–294.
Mottershead, J. E., and Friswell, M. I. (1993). “Model updating in structural dynamics: A survey.” J. Sound Vib., 167(2), 347–375.
Pao, Y. H. (1989). Adaptive pattern recognition and neural networks, Addison-Wesley, Reading, Mass.
Pascual, R., Golinval, J. C., and Razeto, M. (1997). “A frequency domain correlation technique for model correlation and updating.” Proc., 15th Int. Modal Analysis Conf., Orlando, Calif., 587–592
Robitaille, B., Marcos, B., Veillette, M., and Payre, G. (1996). “Modified quasi-Newton methods for training neural networks.” Comput. Chem. Eng., 20(9), 1133–1140.
Reddy, R. R., and Ganguli, R. (2003). “Structural damage detection in a helicopter rotor blade using radial basis function neural networks.” Smart Mater. Struct., 12(2), 232–241.
Rytter, T. (1993). “Vibration based inspection of civil engineering structure.” Ph.D. dissertation, Dept. of Building Technology and Structure Engineering, Aalborg Univ., Aalborg, Denmark.
Sahin, M., and Shenoi, R. A. (2003). “Vibration-based damage identification in beam-like composite laminates by using artificial neural networks.” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 217(6), 661–676.
Sampaio, R. P. C., Maia, N. M. M., and Silva, J. M. M. (1999). “Damage detection using the frequency-response-function curvature method.” J. Sound Vib., 226(5), 1029–1042.
Sampaio, R. P. C., Maia, N. M. M., Silva, J. M. M., and Almas, E. A. M. (2003). “Damage detection in structures: From mode shape to frequency response function methods.” Mech. Syst. Signal Process., 17(3), 489–498.
Schalkoff, R. J. (1997). Artificial neural networks, McGraw-Hill, New York.
Shi, Z. Y., Law, S. S., and Zhang, L. M. (2000). “Damage localization by directly using incomplete mode shapes.” J. Eng. Mech., 126(6), 656–660.
Worden, K. (1997). “Damage detection using a novelty measure.” Proc., 15th Int. Modal Analysis Conf., Orlando, Fla.
Worden, K., Manson, G., and Fieller, N. R. J. (2000). “Damage detection using outlier analysis.” J. Sound Vib., 229(3), 647–667.
Xu, Y. G., Liu, G. R., Wu, Z. P., and Huang, X. M. (2001). “Adaptive multilayer perceptron networks for detection of cracks in anisotropic laminated plates.” Int. J. Solids Struct., 38(32–33), 5625–5645.
Zang, C., Friswell, M. I., and Imregun, M. (2003a). “Structural health monitoring and damage assessment using measured FRFs from multiple sensors. I: The indicator of correlation criteria.” Proc., Damage Assessment of Structures, 245(2), 131–139.
Zang, C., Friswell, M. I., and Imregun, M. (2003b). “Structural health monitoring and damage assessment using measured FRFs from multiple sensors. II: Decision making with RBF networks.” Proc., Damage Assessment of Structures, 245(2), 141–148.
Zang, C., Grafe, H., and Imregun, M. (2001). “Frequency-domain criteria for correlating and updating dynamic finite element models.” Mech. Syst. Signal Process., 15(1), 139–155.
Zang, C., and Imregun, M. (2001a). “Combined neural network and reduced FRF techniques for slight damage detection.” Arch. Appl. Mech., 71(8), 525–536.
Zang, C., and Imregun, M. (2001b). “Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection.” J. Sound Vib., 242(5), 813–827.
Zeng, P. (1998). “Neural computing in mechanics.” Appl. Mech. Rev., 51(2), 173–197.

Information & Authors

Information

Published In

Go to Journal of Engineering Mechanics
Journal of Engineering Mechanics
Volume 133Issue 9September 2007
Pages: 981 - 993

History

Received: Mar 11, 2005
Accepted: Feb 15, 2007
Published online: Sep 1, 2007
Published in print: Sep 2007

Permissions

Request permissions for this article.

Notes

Note. Associate Editor: Joel P. Conte

Authors

Affiliations

Research Fellow, Dept. of Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 2BX, U.K. (corresponding author). E-mail: [email protected]
M. I. Friswell [email protected]
Professor, Dept. of Aerospace Engineering, Univ. of Bristol, Queen’s Building, Bristol BS8 1TR, U.K. E-mail: [email protected]
Professor, Dept. of Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 2BX, U.K. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share