An eigenspace projection clustering method for structural damage detection

  • Zhu, Jun-Hua (MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University) ;
  • Yu, Ling (MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University) ;
  • Yu, Li-Li (MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University)
  • Received : 2011.02.01
  • Accepted : 2012.09.28
  • Published : 2012.10.25


An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.



  1. Alvandi, A. and Cremona, C. (2006), "Assessment of vibration-based damage identification techniques", J. Sound Vib., 292, 179-202.
  2. Bezdek, J.C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, NY.
  3. da Silva, S., Dias Junior, M., Lopes Junior, V. and Brennan, M.J. (2008), "Structural damage detection by fuzzy clustering", Mech. Syst. Signal Pr., 22, 1636-1649.
  4. Farrar, C.R., Duffey, T.A., Doebling, S.W. and Nix, D.A. (2000), "A statistical pattern recognition paradigm for vibration-based structural health monitoring", Proceedings of 2nd International Workshop on Structural Health Monitoring, Stanford, CA, USA.
  5. Farrar, C.R. and Worden, K. (2007), "An introduction to structural health monitoring", Philos. Trans. Royal Soc. A, 365, 303-315.
  6. Fugate, M.L., Sohn, H. and Farrar, C.R. (2001), "Vibration-based damage detection using statistical process control", Mech. Syst. Signal Pr., 15, 707-721.
  7. Gul, M. and Catbas, F.N. (2009), "Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications", Mech. Syst. Signal Pr., 23, 2192-2204.
  8. Jolliffe, I.T. (2002), Principal Component Analysis, Springer, NY.
  9. Koiakowski, P. (2007), "Structural health monitoring-a review with the emphasis on low-frequency methods", Eng. Tran., 55, 239-275.
  10. MATLAB (2000), Toolbox User's Guide,, The MathWorks, Inc..
  11. Nguyen, V.H. and Golinval, J.C. (2010), "Fault detection based on Kernel Principal Component Analysis", Eng. Struct., 32, 3683-3691.
  12. Oh, C.K. and Sohn, H. (2009), "Damage diagnosis under environmental and operational variations using unsupervised support vector machine", J. Sound Vib., 325, 224-239.
  13. Rytter, A. (1993), "Vibration based inspection of civil engineering structures", Aalborg University, Denmark.
  14. Scholkopf, B., Smola, A. and Müller, K.R. (1998), "Nonlinear component analysis as a kernel eigenvalue problem", Neural Comput., 10, 1299-1319.
  15. Sohn, H. (2007), "Effects of environmental and operational variability on structural health monitoring", Philos. Trans. Royal Soc. A, 365, 539-560.
  16. Sohn, H., Farrar, C.R. and Hemez, F.M. (2003), "A review of structural health monitoring literature: 1996- 2001", LA-13976-MS, Los Alamos National Laboratory Report, New Mexico.
  17. Trendafilova, I., Cartmell, M.P. and Ostachowicz, W. (2008), "Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition", J. Sound Vib., 313, 560-566.
  18. Worden, K., Manson, G. and Fieller, N.R.J. (2000), "Damage detection using outlier analysis", J. Sound Vib., 229, 647-667.
  19. Worden, K. and Manson, G. (2007), "The application of machine learning to structural health monitoring", Philos. Trans. Royal Soc. A, 365, 515-537.
  20. Yan, Y., Cheng, L., Wu, Z. and Yam, L. (2007), "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Pr., 21, 2198-2211.
  21. Yu, L. and Xu, P. (2011), "Structural health monitoring based on continuous ACO method", Microelectron. Reliab., 51, 270-278.
  22. Yu, L., Zhu, J.H. and Chen, L.J. (2010), "Parametric study on PCA-based algorithm for structural health monitoring", Proceedings of IEEE Prognostics and Health Management Conference, Macau University, Macau, January.
  23. Zang, C., Friswell, M.I. and Imregun, M. (2003), "Structural health monitoring and damage assessment using measured FRFs from multiple sensors, part I: The indicator of correlation criteria", Proceedings of 5th International Conference on Damage Assessment of Structures, Southampton, England.
  24. Zang, C. and Imregun, M. (2001), "Structural damage detection using artificial neural networks and measured FRF data reduced via principalw component projection", J. Sound Vib., 242, 813-827.

Cited by

  1. System parameter identification from projection of inverse analysis vol.396, 2017,
  2. Detection and parametric identification of structural nonlinear restoring forces from partial measurements of structural responses vol.54, pp.2, 2015,
  3. Nonlinear damage detection using higher statistical moments of structural responses vol.54, pp.2, 2015,
  4. Online damage detection using recursive principal component analysis and recursive condition indicators vol.26, pp.8, 2017,