Automated diagnosis of rolling bearings using MRA and neural networks

Mechanical Systems and Signal Processing

Volumn 24, Issue 1, 2010, Pages 289-299

  Castejon C ^a   Lara, O ^a   Garcia Prada J C ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Artificial networks; Fault diagnosis; Pattern classification; Predictive maintenance; Wavelets

Indexed keywords

ARTIFICIAL NETWORKS; FAULT DIAGNOSIS; PATTERN CLASSIFICATION; PREDICTIVE MAINTENANCE; WAVELETS;

BEARINGS (MACHINE PARTS); FAILURE ANALYSIS; FEATURE EXTRACTION; MAINTENANCE; MULTIRESOLUTION ANALYSIS; NEURAL NETWORKS; VIBRATION ANALYSIS;

BEARINGS (STRUCTURAL);

EID: 70349285991     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2009.06.004     Document Type: Article

References (24)

1. Hu Q., He Z., Zhang Z., and Zi Y. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical System and Signal Processing 21 (2007) 688-705
2. Lou K.L.X. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 18 (2004) 1077-1095
3. Harris T.A. Rolling Bearing Analysis. fourth ed. (2001), A Wiley-Interscience Publication, USA
4. Wang G., Luo Z., Qin X., Leng Y., and Wang T. Fault identification and classification of rolling element bearing based on time-varying autoregressive spectrum. Mechanical Systems and Signal Processing 21 (2007) 934-947
5. Rai V., and Mohanty A. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mechanical Systems and Signal Processing 21 (2007) 2607-2615
6. Du Q., and Yang S. Application of the EMD method in the vibration analysis of ball bearings. Mechanical Systems and Signal Processing 21 (2007) 2634-2644
7. Lei Y., He Z., Zi Y., and Hu Q. Fault diagnosis of rotating machinery based on multiple anfis combination with GAs. Mechanical Systems and Signal Processing 21 (2007) 2280-2294
8. Taoa B., Zhub L., Dinga H., and Xionga Y. An alternative time-domain index for condition monitoring of rolling element bearings. A comparison study. Reliability Engineering and System Safety 92 (2007) 660-670
9. Mallat S. A Wavelet Tour of Signal Processing (1998), Academic Press, New York
10. P.M.W.J. Wang, Application of the wavelet transform to Garvox vibration analysis, American Society of Mechanical Engineers, Petroleum division, 1993, pp. 13-20.
11. Peng Z. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 18 (2004) 199-221
12. Liu B., and Ling S. Machinery diagnostic based on wavelet packets. Journal of Vibration and Control 3 (1997) 5-17
13. Chen B., Wang X., Yang S., and McGreavy C. Application of wavelets and neural networks to diagnostic system development, 1, feature extraction. Computers and Chemical Engineering 23 (1999) 899-906
14. Rainch A., and Cinar A. Statistical process monitoring and disturbance diagnosis in multivariate continuous processes. A.I.Ch.E. Journal 42 (1996) 995-1009
15. Sorsa T., and Koivo H. Application of artificial neural networks in process fault diagnosis. Automatica 29 (1993) 843-849
16. W. Staszewski, Vibration data compression with optimal wavelet coefficients genetic algorithms in engineering systems, in: Innovations and Applications, vol. 446, 1997, pp. 186-189.
17. Chow M., Sharpe R., and Hung J. On the application and design of artificial neural netwoeks for motor fault detection-part I. IEEE Transactions on industrial electronics 40 (1993) 181-188
18. Chow M., Sharpe R., and Hung J. On the application and design of artificial neural netwoeks for motor fault detection-part II. IEEE Transactions on Industrial Electronics 40 (1993) 189-196
19. Scheffer C., and Girdhar P. Practical Machinery Vibration and Predictive Maintenance (2004), Elsevier, Amsterdam
20. J. García-Prada, C. Castejón, O. Lara, Incipient bearing fault diagnosis using DWT for feature extraction, in: 12th IFToMM World Congress, Besançon, France, 2007.
21. Lara O., Castejón C., and García-Prada J. Bearing fault diagnosis based on neural network classification and wavelet transform. WSEAS Transaction on Signal Processing 2 10 (2006) 1371-1378
22. Godoy M., Meireles M., and Almeida P. A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics 50 (2003) 585-601
23. Chow M. Methodologies of using Neural Network and Fuzzy-logic Technologies for Motor Incipient Fault Detection (1997), World Scientific, Singapore
24. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS) 2 4 (1989) 303-314