Fault Classification Using Pseudomodal Energies and Probabilistic Neural Networks
Publication: Journal of Engineering Mechanics
Volume 130, Issue 11
Abstract
This paper introduces a new fault identification method that uses pseudomodal energies to train probabilistic neural networks (PNNs). The proposed procedure is tested on a population of 20 cylindrical shells and its performance is compared to the procedure which uses modal properties to train probabilistic neural networks. The PNNs trained using pseudomodal energies provide better classification of faults than the PNNs trained using the conventional modal properties.
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References
1.
Anderson, H. C. (1980). “Molecular dynamics simulations at constant pressure and/or temperature.” J. Chem. Phys., 72, 2384–2393.
2.
Bellman, R. ( 1961). Adaptive control processes: A guided tour, Princeton University Press, Princeton, N.J.
3.
Bishop, C.M. ( 1995). Neural networks for pattern recognition, Oxford University Press, Oxford, U.K.
4.
Chen, D., and Wang, W. J. (2002). “Classification of wavelet map patterns using multi-layer neural networks for gear fault detection.” Mech. Syst. Signal Process., 16, 695–704.
5.
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 Technical Rep. LA-13070-MS, Los Alamos National Laboratory, Los Alamos, N.M.
6.
Ewins, D.J. ( 1995). Modal testing: Theory and practice, Research Studies Press, Letchworth, U.K.
7.
Haykin, S. ( 1995). Neural networks, Prentice-Hall, New York.
8.
Hinton, G.E. ( 1987). “Learning translation invariant recognition in massively parallel networks.” Proc., PARLE Conf. on Parallel Architectures and Languages, 1–13.
9.
Huisinga, W., Schutte, C., and Stuart, A. M. (2003). “Extracting macroscoping stochastics dynamics: Model problems.” Commun. Pure Appl. Math., 56(2), 234–269.
10.
Jollife, I.T. ( 1986). Principal component analysis, Springer, New York.
11.
Katok, A., and Hasselblatt, B. ( 1995). “Introduction to the modern theory of dynamical systems.” 54, In the series: Encyclopedia of mathematics and its applications, Cambridge University Press, Cambridge, U.K., 54.
12.
Leath, W.J., and Zimmerman, D.C. ( 1993). “Analysis of neural network supervised training with application to structural damage detection.” Damage and Control of Large Structures, Proc., 9th Virginia Polytechnic Institute and State University Symposium, Blacksburg, Va., 583–594.
13.
Mackenzie, P. B. (1989). “An improved hybrid Monte Carlo method.” Phys. Lett. B, 226, 369–371.
14.
Maia, N.M. M., and Silva, J.M. M. ( 1997). Theoretical and experimental modal analysis, Research Studies Press, Letchworth, U.K.
15.
Marwala, T. (2001a). “On fault identification using pseudo-modal-energies and modal properties.” Am. Inst. Aeronaut. Astronaut. J., 39, 1608–1618.
16.
Marwala, T. (2001b) “Probabilistic fault identification using a committee of neural networks and vibration data.” J. Aircr., 38, 138–146.
17.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). “Equations of state calculations by fast computing machines.” J. Chem. Phys., 21, 1087–1092.
18.
Neal, R.M. ( 1993). “Probabilistic inference using Markov chain Monte Carlo methods.” University of Toronto Technical Rep. CRG-TR-93-1, Toronto.
19.
Royton, T. J., Spohnholtz, T., and Ellington, W. A. (2000). “Use of non-degeneracy in nominally axisymmetric structures for fault detection with application to cylindrical geometries.” J. Sound Vib., 230, 791–808.
20.
Wang, F. Y. (2002). “Liouville theorem and coupling on negatively curved Riemannian manifolds.” Stochastic Proc. Appl., 100(1), 27–39.
21.
Worden, K.A., Ball, A., and Tomilinson, G. ( 1993). “Neural networks for fault location.” Proc., 11th Int. Modal Analysis Conf., Orlando, Fla., 47–54.
22.
Yun, C. B., Yi, J. H., and Bahng, E. Y. (2001). “Joint damage assessment of framed structures using a neural networks technique.” Eng. Struct., 23(5), 425–435.
23.
Zubaydi, A., Haddara, M. R., and Swamidas, A. S. J. (2002). “Damage identification in a ship’s structure using neural networks.” Ocean Eng., 29(10), 1187–1200.
Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 130 • Issue 11 • November 2004
Pages: 1346 - 1355
Copyright
Copyright © 2004 ASCE.
History
Published online: Oct 15, 2004
Published in print: Nov 2004
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