Artificial neural network based fault diagnostics of rolling element bearings using time-domain features

Mechanical Systems and Signal Processing

Volumn 17, Issue 2, 2003, Pages 317-328

  Samanta, B ^a   Al Balushi, K R ^a  

^a SULTAN QABOOS UNIVERSITY   (Oman)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; BANDPASS FILTERS; CONDITION MONITORING; FAILURE ANALYSIS; FEATURE EXTRACTION; HIGH PASS FILTERS; NEURAL NETWORKS; ROTATING MACHINERY; TIME DOMAIN ANALYSIS; VIBRATIONS (MECHANICAL); WAVELET TRANSFORMS;

ENVELOPE DETECTION; ROLLING ELEMENT BEARINGS; TIME DOMAIN VIBRATION SIGNALS;

ROLLER BEARINGS;

EID: 0037345899     PISSN: 08883270     EISSN: None     Source Type: Journal    
DOI: 10.1006/mssp.2001.1462     Document Type: Article

References (18)

1. A. C. McCormick and A. K. Nandi 1997 Proceedings of Institution of Mechanical Engineers, Part C 211, 439-450. Classification of the rotating machine condition using artificial neural networks.
2. A. C. McCormick and A. K. Nandi 1997 IEEE Transactions on Neural Networks, 8, 748-756. Real-time classification of the rotating shaft loading conditions using artificial neural networks.
3. A. C. McCormick, A. K. Nandi and L. B. Jack 1998 Proceedings of Institution of Mechanical Engineers, Part C 212, 417-428. Application of periodic time-varying autoregressive models to the detection of bearing faults.
4. I. E. Alguindigue, A. Loskiewicz-Buczak and R. E. Uhrig 1993 IEEE Transactions on Industrial Electronics, 40, 209-216. Monitoring and diagnosis of rolling element bearings using artificial neural networks.
5. B. A. Paya, I. L. Esat and M. N. M. Badi 1997 Mechanical Systems and Signal Processing 11, 751-765. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor.
6. M. Chow, P. M. Mangum and S. O. Yee 1991 IEEE Transactions on Industrial Electronics, 38, 448-453. A neural network approach to real-time condition monitoring of induction motors.
7. J. Shiroishi, Y. Li, S. Liang, T. Kurfess and S. Danyluk 1997 Mechanical Systems and Signal Processing 11, 693-705. Bearing condition diagnostics via vibration and acoustics emission measurements.
8. A. Barkov, N. Barkova and J. Mitchell 1995 Sound and Vibration 28, 10-17 and 27 31. Condition assessment and life prediction of rolling element bearings.
9. A. Azovtsev, A. Barkov and D. Carter 1996 Proceedings of the 20th Annual Meeting of the Vibration Institute, 27-30. Improving accuracy of rolling element bearing condition assessment.
10. T. I. Liu and K. S. Anantharaman 1994 Transaction ASME, Journal of Engineering for Industry, 116, 392-397. Intelligent classification and measurement of drill wear.
11. T. I. Liu, W. Y. Chen and K. S. Anantharaman 1998 Mechanical Systems and Signal Processing 12, 863-873. Intelligent detection of drill wear.
12. Y. Maki and K. A. A. Loparo 1997 IEEE Transactions on Control Systems Technology, 5, 529-541. A neural network approach to fault detection and diagnosis in industrial processes.
13. K. F. Martin and M. H. Marzi 1994 Proceedings of Institution of Mechanical Engineers, Part I 213, 229-241. Diagnostics of a coolant system via neural networks.
14. D. C. Baillie and J. Mathew 1994 Proceedings of International Conference on Condition Monitoring, University of Wales, Swansea, March 21-25, 241-252. Nonlinear model-based fault diagnosis of bearings.
15. R. Ligteringer, R. P. W. Duin, E. E. E. Frietman and A. Ypma 1997 Asci97Setup/paper____970120____2. Machine diagnostics by neural networks, experimental setup.
16. MATHWORKS INC. 1995 MATLAB Reference Guide. MA, U.S.A.
17. D. L. Carter 1995 United States Patent No. 5477730. Rolling element bearing condition testing method and apparatus.
18. D. Ho and R. B. Randall 2000 Mechanical Systems and Signal Processing 14, 763-788, Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals.