Beam damage detection using computer vision technology

In this paper, a new approach for efficient damage detection in engineering structures is introduced. The key concept is to use the mature computer vision technology to capture the static deformation profile of a structure, and then employ profile analysis methods to detect the locations of the damages. By combining with wireless communication techniques, the proposed approach can provide an effective and economical solution for remote monitoring of structure health. Moreover, a preliminary experiment is conducted to verify the proposed concept. A commercial computer vision camera is used to capture the static deformation profiles of cracked cantilever beams under loading. The profiles are then processed to reveal the existence and location of the irregularities on the deformation profiles by applying fractal dimension, wavelet transform and roughness methods, respectively. The proposed concept is validated on both one-crack and two-crack cantilever beam-type specimens. It is also shown that all three methods can produce satisfactory results based on the profiles provided by the vision camera. In addition, the profile quality is the determining factor for the noise level in resultant detection signal.

[1]  M. Rucka,et al.  Crack identification using wavelets on experimental static deflection profiles , 2006 .

[2]  Norris Stubbs,et al.  Damage identification in beam-type structures: frequency-based method vs mode-shape-based method , 2003 .

[3]  Hajime Yamashina,et al.  A machine vision system for measuring wear and chipping of drilling tools , 1996, J. Intell. Manuf..

[4]  A. Cohen.  Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, I. Daubechies, SIAM, 1992, xix + 357 pp. , 1994 .

[5]  C.P.L. Barkan,et al.  Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology , 2006, Proceedings of the 2006 IEEE/ASME Joint Rail Conference.

[6]  Charles R. Farrar,et al.  Variability of modal parameters measured on the alamosa canyon bridge , 1996 .

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  Masayoshi Tomizuka,et al.  Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2012 : 12-15 March 2012, San Diego, California, United States , 2012 .

[9]  David J. Ewins,et al.  MODAL TESTING USING A SCANNING LASER DOPPLER VIBROMETER , 1999 .

[10]  Andrew D. Dimarogonas,et al.  A CONTINUOUS CRACKED BEAM VIBRATION THEORY , 1998 .

[11]  S. Quek,et al.  Sensitivity analysis of crack detection in beams by wavelet technique , 2001 .

[12]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[13]  George J. Vachtsevanos,et al.  A comparison of fractal dimension algorithms using synthetic and experimental data , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[14]  C. Ratcliffe.  DAMAGE DETECTION USING A MODIFIED LAPLACIAN OPERATOR ON MODE SHAPE DATA , 1997 .

[15]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[16]  Leontios J. Hadjileontiadis,et al.  Fractal dimension analysis for crack identification in beam structures , 2005 .

[17]  R. Ruotolo,et al.  Crack detection of a beam using the Wavelet Transform , 1994 .

[18]  C. Farrar,et al.  Effects of measurement statistics on the detection of damage in the Alamosa Canyon Bridge , 1996 .

[19]  Y. Kim,et al.  Damage detection using the Lipschitz exponent estimated by the wavelet transform: applications to vibration modes of a beam , 2002 .

[20]  S. Loutridis,et al.  CRACK IDENTIFICATION IN BEAMS USING WAVELET ANALYSIS , 2003 .

[21]  Xiaomin Deng,et al.  Damage detection with spatial wavelets , 1999 .

[22]  W. J. Wang,et al.  Application of orthogonal wavelets to early gear damage detection , 1995 .

[23]  Jialai Wang.  Damage detection in beams by roughness analysis , 2006, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.