Structural fault detection using a novelty measure

Journal of Sound and Vibration

Volumn 201, Issue 1, 1997, Pages 85-101

  Worden, K ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DEFECTS; FAILURE ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; STIFFNESS;

STRUCTURAL FAULT DETECTION;

STRUCTURAL ANALYSIS;

EID: 0031098084     PISSN: 0022460X     EISSN: None     Source Type: Journal    
DOI: 10.1006/jsvi.1996.0747     Document Type: Article

References (32)

1. C. M. BISHOP 1995 Neural networks for Pattern Recognition. Oxford: Oxford University Press.
2. A. RYTTER 1993 Ph.D. Thesis, Department of Building Technology and Structural Engineering, University of Aalborg, Denmark. Vibration based inspection of civil engineering structures.
3. R. CERAVOLO, A. DE STEFANO, M. GROSJACQUES and D. SABIA 1995 ASME Design Engineering Technical Conference 1127-1133. Experimental identification of structural damage by neural networks.
4. M. F. ELKORDY, K. C. CHANG and G. C. LEE 1993 Americal Society of Civil Engineers, Journal of Computing in Civil Engineering 7, 130-145. Neural network trained by analytically simulated damage states.
5. W. J. LEATH and D. C. ZIMMERMAN 1993 Damage and Control of Large Structures, proceedings of the 9th VPI & SU Symposium 583-594. Analysis of neural network supervised training with application to structural damage detection.
6. R. MANNING 1994 AIAA/ASME Adaptive Structures Forum 160-172. Damage detection in adapative structures using neural networks.
7. P. H. KIRKEGAARD and A. RYTTER 1994 Proceedings of the 12th International Modal Analysis Conference, Hawaii, 1128-1134. Use of neural networks for damage assessment in a steel mast.
8. P. H. KIRKEGAARD and A. RYTTER 1994 Proceedings of the 1st Workshop of the European Group for Structural Engineering Applications of Artificial Intelligence, Lausanne. Vibration based damage assessment of civil engineering structures using neural networks.
9. P. H. KIRKEGAARD and A. RYTTER 1994 Proceedings of 10th International Conference on Experimental Mechanics, Lisbon. Vibration based damage assessment of a cantilever using a neural network.
10. J. KUDVA, N. MUNIR and P. W. TAN 1992 Smart Materials and Structures 1, 108-112. Damage detection in smart structures using a neural networks and finite-element analysis.
11. C. POVICH and T. LIM 1994 AIAA/ASME Adaptive Structures Forum 151-159. An artificial neural network approach to structural damage detection using frequency response functions.
12. J. RHIM and S. LEE 1994 AIAA/ASME Adaptive Structures Forum 173-180. A neural network approach for damage detection in structures.
13. W. SPILLMAN, D. HUSTON, P. FUHR and J. LORD 1993 SPIE Vol. 1918 Smart Sensing, Processing and Instrumentation, 288-295. Neural network damage detection in a bridge element.
14. J. E. STEPHENS and R. D. VANLUCHENE 1994 Microcomputers in Civil Engineering 9, 119-128. Integrated assessment of damage in structures.
15. P. TSOU and M.-H. H. SHEN 1994 American Institute Aeronautics and Astronautics Journal 32, 176-183. Structural damage detection and identification using neural networks.
16. K. WORDEN, A. D. BALL and G. R. TOMLINSON 1993 Smart Materials and Structures 2, 189-200. Fault location in a framework structure using neural networks.
17. K. WORDEN and G. R. TOMLINSON 1994 in Engineering Integrity Assessment (J. H. Edwards, J Kerr and P. Stanley, editors) 11-31. Damage location and quantification using neural networks.
18. X. WU, J. GHABOUSSI and J. H. GARRETT 1992 Computers and Structures 42, 649-659. Use of neural networks in detection of structural damage.
19. P. Z. SZEWCZWK and P. HAJELA 1994 American Society of Civil Engineers Journal of Computing in Civil Engineering 8, 163-178. Damage detection in structures based on feature-sensitive neural networks.
20. G. G. YEN and M. K. KWAK 1993 American Institute Aeronautics and Astronautics 931485-CP, 1549-1555. Neural network approach for the damage detection of structures.
21. G. L. ZHENG and S. A. BILLINGS 1993 Research Report, Department of Automatic Control and Systems Engineering, University of Sheffield. Fault diagnosis using radial basis function neural networks.
22. Y. TEBOUB and P. HAJELA 1992 Proceedings of the 4th AIAA/USAF/NASA/OAI symposium on Multidisciplinary Analysis and Optimisation, Cleveland, OH. A nerual network based damage analysis of smart composite beams.
23. L. TARASSENKO, P. HAYTON, N. CERNEAZ and M. BRADY Preprint, Department of Engineering Sciences, University of Oxford. Novelty detection for the identification of masses in mammograms.
24. S. ROBERTS and L. TARASSENKO 1994 Neural Computation 6, 270-284. A probabilistic resource allocating network for novelty detection.
25. D. A. POMERLEAU 1993 in Advances in Neural Information Processing Systems 5 (S. J. Hanson, J. D. Cowan and C. 1 Giles, editor). Morgan Kaufman. Input reconstruction reliability estimation.
26. C. M. BISHOP 1994 IEE Proceedings on Vision and Image Signal Processing 141, 217-222. Novelty detection and neural network validation.
27. D. E. RUMELHART and J. L. MCCLELLAND 1988 Parallel Distributed Processing: Explorations in the Microstructure of Cognition (two volumes). MIT Press.
28. Q. CHEN, Y. W. CHAN, K. WORDEN and G. R. TOMLINSON 1994 Proceedings of the International Conference on Vibration Engineering, Beijing, China, 567-757. Structural fault detection using neural networks trained on transmissibility functions.
29. W. H. PRESS, B. P. FLANNERY, S. A. TEUKOLSKY and W. T. VETTERLING 1986 Numerical Recipes - The Art of Scientific Computing Cambridge: Cambridge University Press.
30. C. CEMPEL 1985 Maintainence Management International 5, 297-304. Determination of vibration symptom limit values in diagnostics of machinery.
31. A. D. BALL 1991 Ph. D. Thesis, Department of Mechanical Engineering, University of Manchester. Machine condition assessment and fault diagnosis.
32. A. D. BALL, J. E. COOPER and K. WORDEN 1992 in Structural Integrity Assessment, (P. Stanley, editor) 236-245. Elsevier. On-line system identification techniques for structural analysis and monitoring.