Feature selection for damage degree classification of planetary gearboxes using support vector machine

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Volumn 225, Issue 9, 2011, Pages 2250-2264

  Qu, J ^a   Liu, Z ^a,b   Zuo, M J ^a   Huang, H Z ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Damage degree classification; Feature selection; Planetary Gearbox; Support vector machine

Indexed keywords

BENCHMARK DATASETS; BINARY MODELS; DAMAGE DEGREES; DATA SETS; FEATURE DIMENSIONS; FEATURE RANKING; FUSION TECHNIQUES; MULTI-CLASS; MULTICLASS CLASSIFICATION PROBLEMS; PLANETARY GEARBOXES; PLANETARY GEARS; SUPPORT VECTOR; SVM-BASED METHODS; WORK STUDY;

CLASSIFICATION (OF INFORMATION); GEARS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 80052799138     PISSN: 09544062     EISSN: 20412983     Source Type: Journal    
DOI: 10.1177/0954406211404853     Document Type: Conference Paper

References (20)

1. Guyon, I. and Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res., 2003, 3, 1157-1182.
2. Malhi, A. and Gao, R. X. PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas., 2004, 53(6), 1517-1525.
3. Fei, C. G., Han, Z. Z., and Liu, Q. K. Ultrasonic flaw classification of seafloor petroleum transporting pipeline based on chaotic genetic algorithm and SVM. J. X-ray Sci. Technol., 2006, 14, 1-9.
4. Qu, J. and Zuo, M. J. Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement, 2010, 43, 781-791.
5. Vapnik, V. N. Statistical learning theory, 1998 (John Wiley & Sons, New York).
6. Widodo, A. and Yang, B. S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process., 2007, 21, 2560-2574. (Pubitemid 46829756)
7. Samanta, B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech. Syst. Signal Process., 2004, 18(3), 625-644.
8. Khawaja, T. S., Georgoulas, G., and Vachtsevanos, G. An efficient novelty detector for online fault diagnosis based on least squares support vector machines. In IEEE AUTOTESTCON, Salt Lake City, Utah, 8-11 September 2008.
9. Gualdron, O., Brezmes, J., Llobet, E., Amari, A., Vilanova, X., Bouchikhi, B., and Correig, X. Variable selection for support vector machine based multisensor systems. Sens. Actuators B, 2007, 122, 259-268. (Pubitemid 46275419)
10. Cristianini, N. and Taylor, J. S. An introduction to support vector machines and other kernel-based learning methods, 2000 (Cambridge University Press, Cambridge).
11. Scholkopf, B. and Smola, A. Learning with kernels: support vector machines, regularization, and beyond, 2002 (MIT Press, Cambridge, Massachusetts).
12. Vachtsevanos, G., Lewis, F. L., Hess, M. R. A., and Wu, B. Q. Intelligent fault diagnosis and prognosis for engineering systems, 2006 (John Wiley & Sons, Inc, New Jersey).
13. Asuncion, A. and Newman, D. J. UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, California, available from http://www.ics.uci. edu/~mlearn/MLRepository.html.
14. Hoseini, M. and Zuo, M. J. A literature survey on the creating and quantifying faults on planetary gearbox, Technical Report, Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, 2009.
15. Lei, Y. G. and Zuo, M. J. Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mech. Syst. Signal Process., 2009, 23(5), 1535-1547.
16. Lebold, M., McClintic, K., Campbell, R., Byington, C., and Maynard, K. Review of vibration analysis methods for gearbox diagnostics and prognostics. In Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, Virginia, 1-4 May 2000, pp. 623-634.
17. Vecef, P., Kreidl, M., and Smid, R. Condition indicators for gearbox condition monitoring systems. Acta Polytech., 2005, 45(6), 35-43.
18. Zuo, M. J., Li, W., and Fan, X. F. Statistical methods for low speed planetary gearbox monitoring, Technical Report, Department of Mechanical Engineering, University of Alberta, Edmonton, 29 November 2005.
19. Inalpolat, M. and Kahraman, A. A theoretical and experimental investigation of modulation sidebands of planetary gear sets. J. Sound Vib., 2009, 323, 677-696.
20. Decker, H. J. Crack detection for aerospace quality spur gears, NASA/TM-2002-211492, ARL-TR-2682, April 2002.