Structural Damage Detection using Independent Component Analysis

Structural Health Monitoring - Tập 3 Số 1 - Trang 69-83 - 2004
Chaoping Zang1, Michael I. Friswell1, M. Imregun2
1Department of Aerospace Engineering, University of Bristol, Queen’s Building, Bristol BS8 1TR, UK
2Mechanical Engineering Department, Imperial College, Exhibition Road, London SW7 2BX, UK

Tóm tắt

This paper presents a novel approach to detect structural damage based on combining independent component analysis (ICA) extraction of time domain data and artificial neural networks (ANN). The advantage of using time history measurements is that the original vibration information is used directly. However, the volume of data, measurement noise and the lack of reliable feature extraction tools are the major obstacles. To circumvent them, the independent component analysis technique is applied to represent the measured data with a linear combination of dominant statistical independent components and the mixing matrix [ A]. Such a representation captures the essential structure of the measured vibration data. The vibration features represented by the mixing matrix provide the relationship between the measured vibration response and the independent components and are then employed to build the simplified neural network model for damage detection. Two examples are included to demonstrate the effectiveness of the method. First, a truss structure with simulated displacement data was used, and the results show that healthy and damage states located in the nine elements may be classified. Second, a bookshelf structure together with measured time history data from 24 piezoelectric single axis accelerometers was used to demonstrate the approach on a physical structure. The results show the successful detection of the undamaged and damaged states with very good accuracy and repeatability.

Từ khóa


Tài liệu tham khảo

1. 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. Los Alamos National Laboratory Report No. LA-13070-MS.

10.1177/058310249803000201

3. Friswell, M.I. and Penny, J.E.T. (1997). Is damage location using vibration measurements practical? DAMAS 97, Structural damage assessment using advanced signal processing procedures, pp. 351–362, Sheffield, UK .

10.1177/107754639800400505

10.1016/0022-460X(90)90593-O

6. Topole, K.G. and Stubbs, N. (1995) Non-destructive damage evaluation in complex structures from a minimum of modal parameters . International Journal of Analytical and Experimental Modal Analysis, 10(2), 95–103 .

10.1006/jsvi.1997.1302

8. Maia, N.M.M., Silva, J.M.M., Ribeiro, A.M.R. and Sampaio, R.P.C. (1999). On the use of frequencyresponse-functions for damage detection . 2nd International Conference on Identification in Engineering Systems. (pp. 460–471 ), Swansea, UK.

10.1006/jsvi.1999.2340

10. Fritzen, C.P. and Bohle, K. (1999). Identification of damage in large scale structures by means of measured FRFS – procedure and application to the I40-highway-bridge . DAMAS 99, Dublin, Ireland, Key Engineering Materials, ( 167–168, 310–319 ).

10.1016/S0045-7825(97)00016-9

10.1006/jsvi.1997.1308

13. Lew, J.S., Sathananthan, S. and Gu, Y. (1997). Comparison of damage detection algorithms using time domain data. IMAC 15, (pp. 645–651), Orlando, Florida .

10.1006/mssp.1996.0086

15. 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 International Workshop on Structural Health Monitoring. (pp. 764–773), Lancaster, PA: Technomic Publishing .

10.1115/1.3098995

10.1061/(ASCE)0733-9445(2000)126:11(1356)

18. Worden, K. and Manson, G. (1999). Visualization and dimension reduction of high-dimensional data for damage detection. IMAC 17, (pp. 1576–1585), Kissimmee, Florida .

10.1006/jsvi.2000.3390

20. Zang, C. and Imregun, M. (2000). FRF-based structural damage detection using Kohonen self-organizing maps . International Journal of Acoustics and Vibration, 5(4), 167–172 .

10.1007/s004190100154

22. Zang, C., Friswell, M.I. and Imregun, M. (2002). Decomposition of time domain vibration signals using the independent component analysis technique . 3rd International Conference on Identification Engineering System. (pp. 434–445 ), Swansea, UK.

10.1142/S0129065797000458

10.1097/00004728-199903000-00016

25. Rytter, T. (1993). Vibration based inspection of civil engineering structure. PhD Dissertation, Department of Building Technology and Structure Engineering, Aalborg University, Denmark.

26. Lee, T. (1998). Independent component analysis: theory and applications, Kluwer Academic Publishers, Boston .

27. Barlett, M.S., Lades, M.H. and Sejnowski, T.J. (1998). Independent component representation for face recognition . SPIE symposium on electronic imaging: science and technology. (pp. 528–539 ), San Jose, California.

10.1109/97.763148

10.1109/5.720250

10.1162/neco.1997.9.7.1483

10.1109/72.761722

10.1016/0165-1684(95)00042-C

10.1016/S0893-6080(00)00026-5

34. Hyvärinen, A. and Oja, E. (1996). A neuron that learns to separate one independent component from linear mixtures . IEEE International Conference on Neural Networks. (ICNN 96) (pp. 62–67 ), Washington, DC.

35. Bishop, C.M. (1995). Neural networks for pattern recognition, Oxford: Oxford University Press .

36. Masters, T. (1993). Practical neural network recipes in C++. Academic Press, London .

37. Tarassenko, L. (1998). A guide to neural computing applications . Neural Computing Application Forum, Eliane Wigzell.

38. Los Alamos National Laboratory Website: http://www.lanl.gov/projects/damage_id/index.htm

Thông tin
Thông tin xuất bản

Structural Health Monitoring

Tập 3 Số 1

69-83

Thông tin tác giả