Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine

Journal of Vibroengineering

Volumn 14, Issue 2, 2012, Pages 894-906

  Moosavian, Ashkan ^a   Ahmadi, Hojat ^a   Tabatabaeefar, Ahmad ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Condition monitoring; Engine main journal bearing; Fault diagnosis; Fisher linear discriminant; K nearest neighbor; Support vector machine

Indexed keywords

BEARING FAULT DIAGNOSIS; COST BENEFITS; FAULTY CONDITION; FISHER LINEAR DISCRIMINANTS; FREQUENCY DOMAINS; K-NEAREST NEIGHBORS; K-VALUES; MACHINE CONDITION MONITORING; RBF KERNELS; RUNNING-IN; SOLUTION METHODS; VIBRATION PARAMETERS; VIBRATION SIGNAL; VIBRATION TECHNIQUES;

CONDITION MONITORING; FAILURE ANALYSIS; INTERNAL COMBUSTION ENGINES; JOURNAL BEARINGS; VIBRATION ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84865110324     PISSN: 13928716     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (38)

1. Huang B. Detection of abrupt changes of total least square models and application in fault detection. IEEE Transactions on Control Systems Technology, Vol. 9(2), 2001, p. 357-367.
2. Vasylius M. et al. The rotating system vibration and diagnostics. Mechanika, Nr. 4(72), 2008, p. 54-58.
3. Ahmadi H., Moosavian A. Fault diagnosis of journal bearing of generator using power spectral density and fault probability distribution function. In Proc. 1st Conf. INCT, Springer-Verlag, 2011, p. 30-36.
4. Mollazade K. et al. An intelligent combined method based on power spectral density, decision trees and fuzzy logic for hydraulic pumps fault diagnosis. International Journal of Intelligent Systems and Technologies, Vol. 3(4), 2008, p. 251-263.
5. Dabkevičius A., Naginevičiūtė N., Kibirkštis E. Non-destructive identification of vibrations of cardboard boxes. Journal of Vibroengineering, Vol. 13, Issue 4, 2011, p. 886-890.
6. Selection Guide with Bound and Hydro Computer-Assisted Sleeve Bearing Design. Cast Copper Alloy Sleeve Bearings, Copper Development Association.
7. Sun Q., Tang Y. Singularity analysis using continuous wavelet transform for bearing fault diagnosis. Mechanical Systems and Signal Processing, Vol. 16(6), 2002, p. 1025-1041.
8. Baydar N., Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. Mechanical Systems and Signal Processing, Vol. 15, 2001, p. 1091-1107.
9. Rao M. A. J. et al. Coupled torsional-lateral vibration analysis of geared shaft systems using mode analysis. Journal of Sound and Vibration, Vol. 261, 2003, p. 359-364.
10. Andrade F. A., Esat I., Badi M. N. M. A new approach to time domain vibration condition monitoring: gear tooth fatigue crack detection and identification by the Kolmogorov-Smirnov test. Journal of Sound and Vibration, Vol. 24, 2001, p. 909-919.
11. Ho D., Randall R. B. Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical System Signal Processing, Vol. 14, 2000, p. 763-788.
12. Hariharan V., Srinivasan P. S. S. New approach of classification of rolling element bearing fault using artificial neural network. Journal of Mechanical Engineering, Vol. ME 40, Issue 2, 2009, p. 119-130.
13. Mollazade K. et al. Vibration-based fault diagnosis of hydraulic pump of tractor steering system by using energy technique. Modern Applied Science, Vol. 3, Issue 6, 2009, p. 59-66.
14. Rader N. B. A. C. A new principle for fast Fourier transformation. Speech & Signal Processing, Vol. 24, 1976, p. 264-266.
15. Samanta B., Al-Balushi K. R., Al-Araimi S. A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, Vol. 16, 2003, p. 657-665.
16. Janjarasjitt S., Ocakc H., Loparo K. A. Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. Journal of Sound and Vibration, Vol. 3(17), 2008, p. 112-126.
17. Bagheri B., Ahmadi H., Labbafi R. Implementing discrete wavelet transform and artificial neural networks for acoustic condition monitoring of gearbox. Elixir Mech. Engg., Vol. 35, 2011, p. 2909-2911.
18. Belhumeur P. N. et al. Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 7, 1997, p. 717-720.
19. Duda R. O., Hart P. E. Pattern Classification and Scene Analysis. New York, Wiley, 1973.
20. Chiang L. H., Kotanchek M. E., Kordon A. K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering, Vol. 28, 2004, p. 1389-1401.
21. Hasti T., Tibshirani R., Friedman J. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. New York, Springer-Verlag, 2001.
22. Shahabi C., Kolahdouzan M. R., Sharifzadeh M. A road network embedding technique for K-nearest neighbor search in moving object databases. GeoInformatica, Vol. 7(3), 2003, p. 255-273.
23. Song Y. Informative K-nearest neighbor pattern classification. In Proc. Conf. LNAI, Springer-Verlag, 2007, p. 248-264.
24. Duda R. O., Hart P. E., Stork D. G. Pattern Classification. John Wiley & Sons, 2nd ed., 2001.
25. Gunn S. R., Brown M., Bossley K. M. Network performance assessment for neurofuzzy data modeling. Intelligent Data Analysis, Lecture Notes in Computer Science, Vol. 1208, 1997, p. 313-323.
26. Shirvastava S. K., Gharde S. S. Support vector machine for handwritten devanagari numeral recognition. International Journal of Computer Applications, Vol. 7, Issue 11, 2010, p. 237-245.
27. Shigeo A. Support Vector Machine for Pattern Classification. Springer-Verlag, 2005.
28. Vapnik V. Statistical Learning Theory. Springer-Verlag, New York, 1998.
29. Cortes C., Vapnik V. Support-vector network. Machine Learning, Vol. 20, 1995, p. 273-297.
30. Seker S. et al. A neuro-detector based on the cybernetic concepts for fault detection in electric motors. Journal of Vibroengineering, Vol. 13, Issue 4, 2011, p. 629-637.
31. Nese S. V., Kilic O., Akinic T. C. Condition monitoring with signal processing in wind turbines. Journal of Vibroengineering, Vol. 13, Issue 3, 2011, p. 439-445.
32. Igarishi T. Hiroyoshi Studies on vibration and sound of defective rolling bearing. Bulletin JSME, Vol. 25, No. 204, 1980, p. 994-1001.
33. Mechefske C., Mathew J. Fault detection and diagnosis in low speed rolling element bearing. Part II: The use of nearest neighbour classification. Mechanical Systems and Signal Processing, Vol. 6, 1992, p. 309-316.
34. Song Y. et al. Informative K-nearest neighbor pattern classification. In Proc. of the PKDD, LNAI 4702, Springer-Verlag, 2007, p. 248-264.
35. Moosavian A., Ahmadi H., Tabatabaeefar A. Condition monitoring of engine journal bearing using power spectral density and support vector machine. Elixir Mech. Engg., Vol. 43, 2012, p. 6631-6635.
36. Ebrahimi E., Mollazade K. Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system. Insight, Vol. 52, Issue 10, 2010, p. 561-566.
37. Yang B. S., Han T., Hwang W. W. Fault diagnosis of rotating machinery based on multi-class support vector machine. J. Mech. Sci. Technol., Vol. 19, Issue 3, 2005, p. 846-859.
38. Lei Y. et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAS. Mechanical Systems and Signal Processing, Vol. 21, 2007, p. 2280-2294.