An Overview of Intelligent Fault Detection in Systems and Structures

Structural Health Monitoring

Volumn 3, Issue 1, 2004, Pages 85-98

  Worden, K ^a   Dulieu Barton, J M ^b  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Intelligent fault detection; Operational evaluation; Sensor fusion; Sensor optimisation

Indexed keywords

EID: 34547110218     PISSN: 14759217     EISSN: None     Source Type: Journal    
DOI: 10.1177/1475921704041866     Document Type: Article

References (35)

1. Bedworth, M. (1994). Probability moderation for multilevel information processing. DRA Technical Report, DRA/CIS(SE1)/651/8/M94.AS03BP032/1.
2. Bedworth, M. and O'Brien, J. (1999). The Omnibus model: a new model of data fusion. DERA Malvern preprint.
3. Bishop, C.M. (1994). Novelty detection and neural network validation. IEEE Proceedings - Vision and Image Signal Processing, 141, 217-222.
4. Broberg, K.B. (1999). Cracks and fracture. Cambridge University Press.
5. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D. (1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics. Los Alamos National Laboratory Report, LA-13070.
6. Dorey, G. (1989). Damage tolerance and damage assessment in advanced composites. In: Advanced composites (pp. 369-398). Elsevier.
7. Farrar, C.R. and Doebling, S.W. (2000). The state of the art in vibration-based structural damage identification. CD-ROM, Los Alamos Dynamics.
8. Friswell, M.I. and Penny, J.E.T. (1997). Is damage location using vibration measurements practical? Proceedings of 2nd International Conference on Structural Damage Assessment using Advanced Signal Processing Procedures (DAMAS 97), (pp. 351-362), Sheffield.
9. Henry, M.P. and Clarke, D.W. (1993). The self-validating sensor: rationale, definitions and examples. Control Engineering Practice, I, 585-610.
10. Kramers, M.A. (1991). Nonlinear principal component analysis using autoassociative neural networks. American Institute of Chemical Engineers Journal, 37, 233-243.
11. Kramers, M.A. (1992). Autoassociative neural networks. Computers in Chemical Engineering, 16, 313-328.
12. Leitch, R.D. (1995). Reliability analysis for engineers - an introduction. Oxford Science Publications.
13. Lowe, D. and Zapart, C. (1999). Point-wise confidence interval estimation by neural networks: a comparative study based on automotive engine calibration. Neural Computing and Applications, 8, 77-85.
14. Lowe, D. (2000). Feature extraction, data visualisation, classification and fusion for damage assessment. Oral Presentation at EPSRC SID Anet Meeting, Derby, UK.
15. Manson, G., Worden, K. and Allman, D.J. (2002). Experimental validation of a damage severity method. Proceedings of 1st European Workshop on Structural Health Monitoring, (pp. 845-852), Paris.
16. Manson, G., Worden, K. and Allman, D.J. (2003a). Experimental validation of structural health monitoring methodology II: novelty detection on an aircraft wing. Journal of Sound and Vibration, 259, 345-363.
17. Manson, G., Worden, K. and Allman, D.J. (2003b). Experimental validation of structural health monitoring methodology III: Damage location on an aircraft wing. Journal of Sound and Vibration, 259, 365-385.
18. Pomerleau, D.A. (1993). Input reconstruction reliability information. In: Hanson, S.J., Cowan, J.D. and Giles, C.L. (eds.), Advances in neural information processing systems 5, Morgan Kaufman Publishers.
19. Reifsnider, K.L. and Case, S.W. (2002). Damage tolerance and durability of material systems, Wiley Inter-Science.
20. Roberts, S. and Tarassenko, L. (1994). A probabilistic resource allocating network for novelty detection. Neural Computation, 6, 270-284.
21. Rytter, A. (1993). Vibration based inspection of Civil Engineering structures. PhD Thesis, Department of Building Technology and Structural Engineering, University of Aalborg, Denmark.
22. Schalkoff, R. (1992). Pattern recognition: statistical, structural and neural approaches. John Wiley and Sons, Singapore.
23. Side, S., Staszewski, W.J., Wardle, R. and Worden, K. (2000). Fail-safe sensor distributions for damage detection. Proceedings of 2nd International Workshop on Damage Assessment using Advanced Signal Processing Procedures - DAMAS '97, (pp. 41-52), Sheffield, UK.
24. Sohn, H. and Farrar, C.R. (2000). Statistical process control and projection techniques for damage detection. Proceedings of European COST F3 Conference on System Identification and Structural Health Monitoring (pp. 105-114), Madrid, Spain.
25. Staszewski, W.J., Worden, K., Wardle, R. and Tomlinson, G. (2000). Fail-safe sensor distributions for impact detection in composite materials. Smart Materials and Structures, 9, 298-303.
26. Tarassenko, L., Hayton, P., Cerneaz, Z. and Brady, M. (1995). Novelty detection for the identification of masses in mammograms. Proceedings of 4th International Conference on Neural Networks, Cambridge, (pp. 442-447), UK. IEE Publication 409.
27. Taylor, O., MacIntyre, J., Isbell, C., Kirkham, C. and Long, A. (1999). Adaptive fusion devices for condition monitoring: local fusion systems of the NEURAL-MAINE project. Proceedings of 1st International Conference on Damage Assessment of Structures - DAMAS '99, (pp. 205-216), Dublin, Eire.
28. White, F.E. Jr. (1990). Joint Directors of Laboratories Data Fusion Subpanel Report: SIGINT Session. Technical Proceedings of the Joint Service Data Fusion Symposium, DFS-90, 469-484.
29. Worden, K. (1997). Structural fault detection using a novelty measure. Journal of Sound and Vibration, 201, 85-101.
30. Worden, K., Manson, G. and Fieller, N.R.J. (2000). Damage detection using outlier analysis. Journal of Sound and Vibration, 229, 647-667.
31. Worden, K. and Burrows, A.P. (2000). A combined neural and genetic approach to sensor placement. Engineering Structures, 23, 885-901.
32. Worden, K. and Manson, G. (2000). Damage identification using multivariate statistics: kernel discriminant analysis. Inverse Problems in Engineering, 8, 25-46.
33. Worden, K. (2003). Cost action F3 on structural dynamics: benchmarks for working group 2 - structural health monitoring. Mechanical Systems and Signal Processing, 17, 73-75.
34. Worden, K., Manson, G. and Allman, D.J. (2003). Experimental validation of structural health monitoring methodology I: novelty detection on a laboratory structure. Journal of Sound and Vibration, 259, 323-343.
35. Zehnder, A.T. and Rosakis, A.J. (1993). Temperature rise at the tip of dynamically propagating cracks: measurements using high speed IR detectors. In: Experimental techniques in fracture, (pp. 125-169), V.C.H. Publishers Inc., New York.