Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm

Current Science

Volumn 90, Issue 4, 2006, Pages 542-548

  Marwala, Tshilidzi ^a   Chakraverty, S ^b  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^b CSIR CENTRAL BUILDING RESEARCH INSTITUTE   (India)

Author keywords

Fault classification; Genetic algorithm; Neural networks

Indexed keywords

EID: 33644880300     PISSN: 00113891     EISSN: 00113891     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. Zang, C. and Imregun, M., Combined neural network and reduced FRF techniques for slight damage detection using measured response data. Arch. Appl. Mech., 2001, 71, 525-536.
2. Waszczyszyn, Z. and Ziemianski, L., Neural networks in mechanics of structures and materials - new results and prospects of applications. Comp. Struct., 2001, 79, 2261-2276.
3. Fang, X., Luo, H. and Tang, J., Structural damage detection using neural network with learning rate improvement. Comp. Struct., 2005, in press.
4. Marwala, T., Fault classification using pseudo modal energies and probabilistic neural networks. J. Eng. Mech., 2004, 130, 1346-1355.
5. Marwala, T., Fault classification using pseudo modal energies and neural networks. Am. Inst. Aero. Astron. J., 2003, 41, 82-89.
6. Szewczyk, Z. P. and Hajela, P., Damage detection in structures based on feature sensitive neural networks. ASCE J. Comput. Civil Eng., 1994, 8, 163-179.
7. Doebling, S. W., Farrar, C. R. and Prime, M. B., Summary review of vibration-based damage identification methods. Shock Vibration Digest, 1998, 30, 91-105.
8. Bishop, C. M., Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
9. Marwala, T., Fault identification using neural networks and vibration data. Ph D thesis, University of Cambridge, UK, 2000.
10. Lopes, V., Park, G., Cudney, H. H. and Inman, D. J., Impedance-based structural health monitoring with artificial neural networks. J. Intelligent Mater., Syst. Struct., 2000, 11, 206-216.
11. Marwala, T., Probabilistic fault identification using a committee of neural networks and vibration data. J. Aircraft, 2001, 38, 138-146.
12. Neal, R. M., Probabilistic inference using Markov chain Monte Carlo methods, University of Toronto Technical Report CRG-TR-93-1, Toronto, Canada, 1993.
13. Chen, Q., Chan, Y. W. and Worden, K., Structural fault diagnosis and isolation using neural networks based on response only data. Comp. Struct., 2003, 81, 2165-2172.
14. Wu, X., Ghaboussi, J. and Garret, J. H., Use of neural networks in detection of structural damage. Comp. Struct., 1992, 42, 649-659.
15. Levin, R. I. and Lieven, N. A. J., Dynamic finite element updating using neural networks. J. Sound Vibr., 1998, 210, 593-608.
16. Atalla, M. J. and Inman, D. J., On model updating using neural networks. Mech. Syst. Signal Process., 1998, 12, 135-161.
17. Marwala, T. and Hunt, H. E. M., Fault identification using finite element models and neural networks. Mech. Syst. Signal Process., 1999, 13, 475-490.
18. Suresh, S., Omkar, S. N., Ganguli, R. and Mani, V., Identification of crack location and depth in a centilever beam using a modular neural network approach. Smart Mater. Struct., 2004, 13, 907-916.
19. Reddy, R. R. K. and Ganguli, R., Structural damage detection in a helicopter rotor using radial basis function neural networks. Smart Struct. Mater., 2003, 12, 232-241.
20. Pawar, P. P. and Ganguli, R., Genetic fuzzy system for damage detection in beams and helicopter rotor blades. Comp. Method. Appl. Mech. Eng., 2003, 192, 2031.
21. Lu, P. J. and Hsu, T. C., Application of autoassociative neural network on gas-path sensor data validation. J. Propul. Power, 2002, 18, 879-888.
22. Kramer, M. A., Autoassociative neural networks. Comp. Chem. Eng., 1992, 16, 313-328.
23. Møller, M., A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6, 525-533.
24. Hines, J. W., Uhrig, R. E. and Wrest, D. J., Use of autoassociative neural networks for signal validation. J. Intelligent Robotic Syst., 1998, 21, 143-154.
25. Kramer, M. A., Nonlinear principal component analysis using autoassociative neural Networks. AIChE J., 1991, 37, 233-234.
26. Upadhyaya, B. R. and Eryurek, E., Application of neural networks for sensor validation and plant monitoring. Nuclear Technol., 1992, 97, 170-176.
27. Jensen, C. A., El-Sharkawi, M. A. and Marks II R. J., Power system security assessment using neural networks: feature selection using Fisher discrimination. IEEE Trans. Energy Conversion, 2001, 16, 757-763.
28. Reed, R. D. and Marks II R. J., Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, MIT Press, Cambridge, MA, 1999.
29. Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
30. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
31. Ewins, D. J., Modal Testing: Theory and Practice, Research Studies Press, Letchworth, UK, 1995.
32. Royston, T. J., Spohnholtz, T. and Ellington, W. A., Use of non-degeneracy in nominally axisymmetric structures for fault detection with application to cylindrical geometries. J. Sound Vibration, 2000, 230, 791-808.
33. Maia, N. M. M. and Silva, J. M. M., Theoretical and Experimental Modal Analysis, Research Studies Press, Letchworth, UK, 1997.