Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing

International Journal of Knowledge-Based and Intelligent Engineering Systems

Volumn 9, Issue 2, 2005, Pages 149-157

  Liu, Tien I ^a   Ordukhani, Farhad ^a   Jani, Dipak ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85013611443     PISSN: 13272314     EISSN: 18758827     Source Type: Journal    
DOI: 10.3233/KES-2005-9209     Document Type: Article

References (30)

1. D. Bently, An Effective way to Monitor and Analyze Rolling Element Bearing Condition, Bently Nevada Corporation, Minden, 1989.
2. D. Bently, Predictive Maintenance Through the Monitoring and Diagnostics of Rolling Element Bearings, Bently Nevada Corporation, Minden, 1989.
3. B. Li, G. Goddu and M.Y. Chow, Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach, Proc. American Control Conf., 1998, p. 2032-2036.
4. D. Dyer and M. Stewart, Detectionof rolling element bearing damage by statistical vibration analysis, Journal of Mechanical Design 100 (1978), 229-236.
5. J.C. Li and S.M. Wu, On-Line detection of Localized Defects in Bearings by Pattern Recognition Analysis, Journal of Engineering for Industry (Nov., 1989), 331-336.
6. J. Donelson III. and R.L. Dicus, Bearing Defect Detection Using On-board Accelerometer Measurements, ASME/IEEE Joint Railroad Conference, 23-25, 2002, p. 95-102.
7. T.I. Liu and N.R. Iyer, Diagnosisof roller bearing defects using neural networks, International Journal of Advanced Manufacturing Technology 8 (1993), 210-215.
8. T.I. Liu and J.M. Mengel, Intelligentmonitoring of ball bearing conditions, Journal of Mechanical Systems and Signal Processing 6(5) (1992), 419-431.
9. T.I. Liu, J.H. Singonahalli and N.R. Iyer, Detectionof roller bearing defects using expert system and fuzzy logic, Journal of Mechanical Systems and Signal Processing 10(5) (1996), 595-614.
10. B. Li, M.Y. Chow, Y. Tipsuwan and J.C. Hung, Neuralnetwork-based motor rolling bearing fault diagnosis, IEEE Transactions On Industrial Electronics 47(5) (2000), 1060-1069.
11. F.D. McFadden and J.D. Smith, Vibrationmonitoring of rolling element bearings by the high frequency resonance technique, Journal of Tribology (1984), 3-10.
12. M.Y. Chow, Methodologies of Using Neural Network and Fuzzy Logic for Motor Incipient Fault Detection, World Scientific, Singapore, 1997.
13. V.B. Jammu, K. Danai and D.G. Lewicki, Experimentalevaluation of a structural-based connectionist network for fault diagnosis of helicopter gearboxes, ASME Journal of Mechanical Design 120(1) (1998), 106-112.
14. P.T. Kazlas, P.T. Monsen and M.J. LeBlanc, Neural Network-Based Helicopter Gearbox Health Monitoring System, IEEESP Workshop on Neural Networks for Signal Processing, 1993, Linthicum, Maryland.
15. T.I. Liu and K.S. Anatharaman, Intelligentclassification and measurement of drill wear, ASME Journal of Engineering for Industry 16(3) (1994), 392-397.
16. T.I. Liu, W.Y. Chen and K.S. Anantharaman, Intelligentdetection of drill wear, Journal of Mechanical Systems and Signal Processing 12(6) (1998), 863-873.
17. T.I. Liu, A. Kumagai, Y.C. Wang, S.D. Song and Z.Fu, Intelligent Detection of Boring Tool Conditions, Proceedings of ASMEIMECE Symposium on Advances in Tooling andWork Holding Devices, 2003, p. 1-7.
18. Y.M. Niu, Y.S.Wong, G.S. Hong and T.I. Liu, Multi-category classification of tool conditions using wavelet packets and ART2 network, ASME Journal of Manufacturing Science and Engineering 112 (1998), 807-816.
19. S. Rangwala and D. Dornfeld, Sensorintegration using neural networks for intelligent tool condition monitoring, ASME Journal of Engineering for Industry 112 (1990), 219-228.
20. S. Haykin, Neural Networks-A Comprehensive Foundation, Englewood Cliffs, Prentice-Hall, New Jersey, 1999.
21. D. Rumelhart and J. McClelland, Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.
22. Demuth and M. Beale, User’s Guide for Neural Network Toolbox for Use with MATLAB, The MathWorks Inc., Natick, MA, 1998.
23. S. Judd, Neural Network Design and the Complexity of Learning, MIT Press, Cambridge, 1990.
24. R. Mooney, J. Shavlik, G. Towell and A. Gove, An Experimental Comparison of Symbolic and Connectionist learning Algorithms, Kaufmann Publishers Inc., Morgan, San Mateo, 1990.
25. S. Grossberg, Neural Networks and Natural Intelligence, MIT Press, Cambridge, 1988.
26. R. Hecht-Nielson, HNC Explorenet 3000 Operating Manual, HNC Neurocomputer Corporation, San Diego, 1991.
27. J.S.R. Jang, C.T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, New Jersey, 1997.
28. T.M. Cover, Geometricaland statistical properties of systems of linear inequalities with applications in pattern recognition, IEEE Transaction on Computers 14 (1965), 326-334.
29. P.A. Devijver and J. Kittler, Pattern Recognition-A Statistical Approach, Prentice Hall, New Jersey, 1959.
30. A. Whitney, A direct method of Non-parametric measurement selection, IEEE Transactions on Computers 20 (1971), 1100-1103.