Damage Classification in CFRP Laminates Using Principal Component Analysis (PCA) Approach

Abstract:

Article Preview

Signal processing is an important element used for identifying damage in any SHM-related application. The method here is used to extract features from the use of different types of sensors, of which there are many. The responses from the sensors are also interpreted to classify the location and severity of the damage. This paper describes the signal processing approaches used for detecting the impact locations and monitoring the responses of impact damage. Further explanations are also given on the most widely-used software tools for damage detection and identification implemented throughout this research work. A brief introduction to these signal processing tools, together with some previous work related to impact damage detection, are presented and discussed in this paper.

Info:

Periodical:

Main Theme:

Edited by:

R. Varatharajoo, E. J. Abdullah, D. L. Majid, F. I. Romli, A. S. Mohd Rafie and K. A. Ahmad

Pages:

189-194

Citation:

M. T. H. Sultan et al., "Damage Classification in CFRP Laminates Using Principal Component Analysis (PCA) Approach", Applied Mechanics and Materials, Vol. 225, pp. 189-194, 2012

Online since:

November 2012

Export:

Price:

$38.00

[1] G. Manson, K. Worden, K. Holford, and R. Pullin, Visualisation and dimension reduction of acoustic emission data for damage detection, Journal of Intelligent Material Systems and Structures, 2001, 12(8), pp.529-536.

DOI: https://doi.org/10.1177/10453890122145375

[2] P. De Boe, and J.C. Golinval, Principal Component Analysis of a piezosensor array for damage localization, Structural Health Monitoring, 2003, 2(2), pp.137-144.

DOI: https://doi.org/10.1177/1475921703002002005

[3] K. Pearson, On Lines and Planes of Closest Fit to Systems of Points in Space. 1901, Philosophical Magazine 2 (6), pp.559-572.

DOI: https://doi.org/10.1080/14786440109462720

[4] I.T. Jolliffe, Principal Component Analysis. Springer-Verleg, New York, (1986).

[5] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, (1995).

[6] R. Shao, F. Jia, E.B. Martin, and A.J. Morris, Wavelets and non-linear principal components analysis for process monitoring, Control Engineering Practice, 1999, 7(7), pp.865-879.

DOI: https://doi.org/10.1016/s0967-0661(99)00039-8

[7] P. Kadlec, B. Gabrys, and S. Strandt, Data-driven soft sensors in the process industry, Computers & Chemical Engineering, 2009, 33(4), pp.795-814.

DOI: https://doi.org/10.1016/j.compchemeng.2008.12.012

[8] C. Zang and M. Imregun, Combined neural network and reduced FRF techniques for slight damage detection using measured response data, Archive of Applied Mechanics, 2001, 71(8), pp.525-536.

DOI: https://doi.org/10.1007/s004190100154

[9] K. Worden, G. Manson, and D. Allman, Experimental validation of a structural health monitoring methodology: Part I. Novelty detection on a laboratory structure, Journal of Sound and Vibration, 2003, 259(2), pp.323-343.

DOI: https://doi.org/10.1006/jsvi.2002.5168

[10] G. Manson, K. Worden, and D. Allman, Experimental validation of a structural health monitoring methodology: Part II. Novelty detection on a Gnat aircraft, Journal of Sound and Vibration, 2003, 259(2), pp.345-363.

DOI: https://doi.org/10.1006/jsvi.2002.5167

[11] S. Sharma. Applied Multivariate Techniques, John Wiley & Sons, Inc., New York, (1996).

[12] C. Zang, and M. Imregun, Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection, Journal of Sound and Vibration, 2001, 242(5), pp.813-827.

DOI: https://doi.org/10.1006/jsvi.2000.3390

[13] M.T.H. Sultan, K. Worden, S.G. Pierce, D. Hickey, W.J. Staszewski, and J.M. Dulieu-Barton, Impact Damage Detection and Quantification for CFRP Laminates, Proceeding of the 10th Deformation of Composites (DFC10), 15-17 April 2009, Sheffield, UK.

[14] M.T.H. Sultan, K. Worden, W.J. Staszewski, S.G. Pierce, J.M. Dulieu-Barton, and A. Hodzic, Impact Damage Detection and Quantification in CFRP Laminates; A Precursor to Machine Learning, Structural Health Monitoring, From System Integration to Autonomous System, 2009, Vol. 1, pp.1528-1537.

DOI: https://doi.org/10.1016/j.ymssp.2011.05.014

[15] M.T.H. Sultan, K. Worden, J.M. Dulieu-Barton, W.J. Staszewski, A. Hodzic, and S.G. Pierce, Identification of impact damage in CFRP laminates using the NDT approach, Proceedings of the AEROTECH III, 18-19 November 2009, Kuala Lumpur, Malaysia.

[16] M.T.H. Sultan, W.J. Staszewski, and K. Worden, Wavelet Feature Extraction for Impact Damage Analysis for CFRP Laminates, Proceedings of 10th International Conference on Recent Advances in Structural Dynamics, 12-14 July 2010, Southampton, UK.

[17] M.T.H. Sultan, A. Hodzic, W.J. Staszewski, and K. Worden, A SEM-Based Study of Structural Impact Damage, Applied Mechanics and Materials, 2010, Vol. 24-25, pp.233-238.

DOI: https://doi.org/10.4028/www.scientific.net/amm.24-25.233

[18] K.S. Ho, S.G. Pierce, M.H. Li, G. Hayward, and M.T.H. Sultan, The Improvement of the Reliability in Imaging using the Bayesian Approach, IEEE International Ultrasonics Symposium (IUS), 11-14 October 2010, San Diego, California.

[19] M.T.H. Sultan, K. Worden, and W.J. Staszewski, Damage Detection in CFRP Laminates using a Statistical Method, Sensor, Instrumentation and Special Topics, Conference Proceeding of the Society for Experimental Mechanics Series 9, 2011, Vol. 6, pp.91-101.

DOI: https://doi.org/10.1007/978-1-4419-9507-0_11

[20] M.T.H. Sultan, K. Worden, S.G. Pierce, D. Hickey, W.J. Staszewski, J.M. Dulieu-Barton, and A. Hodzic, On Impact Damage Detection and Quantification for CFRP Laminates, Mechanical System and Signal Processing. 2011, Vol 25(8), pp.3135-3152.

DOI: https://doi.org/10.1016/j.ymssp.2011.05.014