Structural damage identification via a combination of blind feature extraction and sparse representation classification

Mechanical Systems and Signal Processing

Volumn 45, Issue 1, 2014, Pages 1-23

  Yang, Yongchao ^a   Nagarajaiah, Satish ^a,b  

^a Rice University   (United States)

^b RICE UNIVERSITY   (United States)

Author keywords

Blind source separation; Classification; Compressed sensing; Damage identification; Sparse representation; Structural health monitoring

Indexed keywords

CLASSIFICATION FRAMEWORK; DAMAGE IDENTIFICATION; FEATURE EXTRACTION AND CLASSIFICATION; LINEAR COMBINATIONS; SPARSE REPRESENTATION; STRUCTURAL DAMAGE IDENTIFICATION; TWO-STAGE PROCEDURES; UNDERDETERMINED LINEAR SYSTEMS;

BLIND SOURCE SEPARATION; CLASSIFICATION (OF INFORMATION); COMPRESSED SENSING; DAMAGE DETECTION; LINEAR SYSTEMS; SIGNAL RECONSTRUCTION; STRUCTURAL HEALTH MONITORING;

FEATURE EXTRACTION;

EID: 84891825865     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2013.09.009     Document Type: Article

References (53)

1. A. Rytter, Vibration Based Inspection of Civil Engineering Structures (Ph.D. thesis), Department of Building Technology and Structural Engineering, Aalborg University, Denmark, 1993.
2. S.G. Braun, D.J. Ewins, and S.S. Rao Encyclopedia of Vibration 2001 Academic Press New York
3. S.W. Doebling, C.R. Farrar, M.B. Prime, D.W. Shevitz, Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in their Vibration Characteristics: a Literature Review. Report LA-13070-MS, Los Alamos National Laboratory, Los Alamos, NM, 1996.
4. A. Hera, and Z. Hou Application of wavelet approach for ASCE structural health monitoring benchmark studies ASCE J. Eng. Mech. 130 2004 96 104
5. S. Law, X. Li, X. Zhu, and S. Chan Structural damage detection from wavelet packet sensitivity Eng. Struct. 27 2005 1339 1348
6. B. Basu, S. Nagarajaiah, and A. Chakraborty Online identification of linear time-varying stiffness of structural systems by wavelet analysis Int. J. Struct. Health Monit. 7 2008 21 36
7. J.N. Yang, Y. Lei, S. Lin, and N. Huang Hilbert-Huang based approach for structural damage detection ASCE J. Eng. Mech. 130 2004 85 95
8. N. Roveri, and A. Carcaterra Damage detection in structures under traveling loads by Hilbert-Huang transform Mech. Syst. Signal Process. 28 2012 128 141
9. M. Feldman, and S. Braun Identification of non-linear system parameters via the instantaneous frequency: application of the Hilbert transform and Wigner-Ville technique Proceedings of the 13th International Modal Analysis Conference (IMAC), Nashville, 1995 pp.637 642
10. S. Nagarajaiah, and B. Basu Output only identification and structural damage detection using time frequency and wavelet techniques Earthq. Eng. Eng. Vib. 8 2009 583 605
11. L.J. Hadjileontiadis, E. Douka, and A. Trochidis Fractal dimension analysis for crack identification in beam structures Mech. Syst. Signal Process. 19 2005 659 674
12. H. Li, Y. Huang, J. Ou, and Y. Bao Fractal dimension-based damage detection method for beams with a uniform cross section Comput.-Aided Civil Infrastruct. Eng. 26 2011 190 206
13. J. Antoni Cyclostationarity by examples Mech. Syst. Signal Process. 23 2009 987 1036
14. C. Zang, M.I. Friswell, and M. Imregun Structural damage detection using independent component analysis Int. J. Struct. Health Monit. 3 2004 69 83
15. J. Antoni, and S. Braun Special issue: blind source separation Mech. Syst. Signal Process. 19 2005 1163 1380
16. Y. Yang, S. Nagarajaiah, Blind identification of damage in time-varying system using independent component analysis with wavelet transform, Mech. Syst. Signal Process., 2012 (in press), doi:10.1016/j.ymssp.2012.08.029.
17. M.I. Friswell, and J.E. Mottershead Finite Element Model Updating in Structural Dynamics 1995 Kluwer Academic Publishers London
18. I. Bucher, and S.G. Braun The structural modification inverse problem: an exact solution Mech. Syst. Signal Process. 7 1993 217 238
19. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press Oxford
20. Z. Sun, and C.C. Chang Structural damage assessment based on wavelet packet transform ASCE J. Struct. Eng. 128 2002 1354 1361
21. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167
22. K. Worden, and A.J. Lane Damage identification using support vector machines Smart Mater. Struct. 10 2001 540
23. A. Widodo, and B. Yang Support vector machine in machine condition monitoring and fault diagnosis Mech. Syst. Signal Process. 21 2007 2560 2574
24. A. Widodo, and B. Yang Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors Expert Syst. Appl. 33 2007 241 250
25. S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani Rolling element bearing classification based on the wavelet denoising and support vector machine Mech. Syst. Signal Process. 21 2007 2933 2945
26. I. Trendafilova, M.P. Cartmell, and W. Ostachowicz Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition J. Sound Vib. 313 2008 560 566
27. P. Dharap, B.H. Koh, and S. Nagarajaiah Structural health monitoring using ARMarkov observers J. Intell. Mater. Syst. Struct. 17 2006 469 481
28. P. Comon, and C. Jutten Handbook of Blind Source Separation: Independent Component Analysis and Applications 2010 Academic Press New York, NY
29. A.M. Bruckstein, D.L. Donoho, and M. Elad From sparse solutions of systems of equations to sparse modeling of signals and images SIAM Rev. 51 2008 34 81
30. E. Candès, J. Romberg, and T. Tao Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency domain IEEE Trans. Inf. Theory 52 2006 489 509
31. D.L. Donoho Compressed sensing IEEE Trans. Inf. Theory 52 2006 1289 1306
32. J.V. Stone Blind source separation using temporal predictability Neural Comput. 13 2001 1559 1574
33. A. Hyvärien, J. Karhunen, and E. Oja Independent Component Analysis 2001 Wiley New York, NY
34. A. Belouchrani, A.K. Abed-Meraim, J.F. Cardoso, and E. Moulines A blind source separation technique using second-order statistics IEEE Trans. Signal Process. 45 1997 434 444
35. J. Antoni Blind separation of vibration components: principle and demonstrations Mech. Syst. Signal Process. 19 2005 1166 1180
36. G. Kerschen, F. Poncelet, and J-C. Golinval Physical interpretation of independent component analysis in structural dynamics Mech. Syst. Signal Process. 21 2007 1561 1575
37. F. Poncelet, G. Kerschen, J.-C. Golinval, and D. Verhelst Output-only modal analysis using blind source separation techniques Mech. Syst. Signal Process. 21 2007 2335 2358
38. W. Zhou, and D. Chelidze Blind source separation based vibration mode identification Mech. Syst. Signal Process. 21 2007 3072 3087
39. S.I. McNeill, and D.C. Zimmerman A framework for blind identification using joint approximate diagonalization Mech. Syst. Signal Process. 22 2008 1526 1548
40. B. Hazra, A.J. Roffel, S. Narasimhan, and M.D. Pandy Modified cross-correlation method for the blind identification of structures ASCE J. Eng. Mech. 136 2010 889 897
41. Y. Yang, and S. Nagarajaiah Time-frequency blind source separation using independent component analysis for output-only modal identification of highly-damped structures ASCE J. Struct. Eng. 139 2012 1780 1793
42. J. Antoni, and S. Chauhan A study and extension of second-order blind source separation to operational modal analysis J. Sound Vib. 332 2013 1079 1106
43. Y. Yang, and S. Nagarajaiah Blind identification of modal parameters in time domain based on complexity pursuit Earthq. Eng. Struct. Dyn. 42 2013 1885 1905
44. M. Lustig, J.M. Santos, D.L. Donoho, and J.M. Pauly Sparse MRI: the application of compressed sensing for rapid MR imaging Magn. Reson. Med. 58 2007 1182 1195
45. J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Trans. Pattern Anal. Mach. Intell. 31 2009 210 227
46. Y. Bao, J.L. Beck, and H. Li Compressive sampling for accelerometer signals in structural health monitoring Int. J. Struct. Health Monit. 10 2011 235 246
47. Y. Yang, and S. Nagarajaiah Output-only modal identification with limited sensors using sparse component analysis J. Sound Vib. 332 2013 4741 4765
48. D. Mascarenas, A. Cattaneo, J. Theiler, C. Farrar. Compressed sensing techniques for detecting damage in structures, Int. J. Struct. Health Monit. 2013, (in press), doi:10.1177/1475921713486184.
49. S.M. O'Connor, J.P. Lynch, A.C. Gilbert. Implementation of a compressive sampling scheme for wireless sensors to achieve energy efficiency in a structural health monitoring system, SPIE Smart Structures and Materials+Nondestructive Evaluation and Health Monitoring, San Diego, CA, 2013.
50. S. Xie, Z. He, and Y. Fu A note on Stone's conjecture of blind source separation Neural Comput. 17 2005 321 330
51. S.S. Chen, D.L. Donoho, and M.A. Saunders Atomic decomposition by basis pursuit SIAM J. Sci. Comput. 20 1999 33 61
52. ℓ1-Magic: Recovery of Sparse Signals Via Convex Programming, 〈http://www.acm.caltech.edu/l1magic/ 〉, 2005.
53. ℓ1-regularized least squares IEEE J. Sel. Top. Signal Process. 1 2007 606 617