A variable kernel classifier using alopex optimization and its application to bearing fault diagnosis

Proceedings of the Sixth IASTED International Conference on Modelling, Simulation, and Optimizatiom, MSO 2006

Volumn , Issue , 2006, Pages 56-61

  Kith, Kimcheng ^a   Van Wyk, Barend L ^a   Van Wyk, Michál A ^a  

^a TSHWANE UNIVERSITY OF TECHNOLOGY   (South Africa)

Author keywords

ALOPEX; Bearing data classification; Generalized variable kernel similarity metric; Metric learning; Variable kernel similarity metric

Indexed keywords

DATA PROCESSING; FAILURE ANALYSIS; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; STOCHASTIC CONTROL SYSTEMS;

ALOPEX OPTIMIZATION; KERNEL CLASSIFIER; METRIC LEARNING; STATISTICAL FEATURES; STOCHASTIC OPTIMIZATION;

BEARINGS (STRUCTURAL);

EID: 38049110046     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Naudé, J.J., van Wyk, M.A., Generalized Similarity Metric Learning for a Variable-Kernel Classifier., Proceedings of the Thirteenth annual symposium of the Pattern Recognition Association of South Africa, November 2002.
2. Lowe, D. G., Similarity Metric Learning for a Variable-Kernel Classifier, Neural Computation, 7(1), 72-85, 1995.
3. Ligteringen, R., Duin, R.P.W., Frietman, E.E.E., Ypma, A. - Machine diagnostics by neural networks: experimental setup, Proceedings of ASCI97, Heijen (The Netherlands), June 2-4, 1997
4. Nikolaou N.G., and Antoniadis I.A., Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets, Mechanical Systems and Signal Processing, 16(4), 677-694, 2002.
5. Chen C. and Mo C., A method for intelligent fault diagnosis of rotating machinery, Digital Signal Processing, 14, 203-217, 2004.
6. Lou X. and Loparo K.A., Bearing fault diagnosis based on wavelet transform and fuzzy inference, Mechanical Systems and Signal Processing, 18, 1077-1095, 2004.
7. Altman J. and Mathew J., Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis, Mechanical Systems and Signal Processing, 15(5), 963-977, 2001.
8. Zhang S., Matyhew J., Ma L., Sun Y., Best basisbased intelligent machine fault diagnostics, Mechanical Systems and Signal Processing, 19, 357-370, 2005.
9. Sun Q. and Tang Y., Singularity analysis using continuous wavelet transform for bearing fault diagnosis, Mechanical Systems and Signal Processing, 16(6), 1025-1041, 2002.
10. Prabhakar S., Mohanty A.R., Sekhar A.S., Application of discrete wavelet transform for detection of ball bearing race faults, Tribology International, 35, 793-800, 2002.
11. Subrahmanyam M. and Sujatha C., Using neural networks for the diagnosis of localized defects in ball bearings, Tribology International, 30(10), 739-752, 1997.
12. Kowalski C.T. and Orlowska-Kowalska T., Neural networks application for induction motor fault diagnosis, Mathematics and Computers in Simulation, 63, 435-448, 2003.
13. Spoerre J.K., Application of the cascade correlation algorithm (CCA) to bearing fault classification problems, Computers in Industry, 32, 295-304, 1997.
14. Gelle G. and Colas M., Blind source separation: A tool for rotating machine monitoring by vibration analysis, Journal of Sound and Vibration, 248(5), 865-885, 2001.
15. Zhang L., Jack. L.B., and Nandi A.K., Fault detection using genetic programming, Mechanical Systems and Signal Processing, 19, 271-289, 2005.
16. Samanta B., Al-Balushi K.R. and Al-Araimi S.A., Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence, 16, 657-665, 2003.
17. Samanta B., and Al-Balushi K.R., Artificial neural network based fault diagnostics of rolling element bearings using time-domian features.
18. E. Tzanakou and E. Hart, Determination of visual receptive fields by stochastic methods, Biophys. J., 15:42a, 1973
19. Alejandro Bia, A Study of Possible Improvements to the Alopex Training Algorithm, VI Brazilian Symposium on Neural Networks (SBRN'OO), pp. 125-130, 2000