An enhanced diagnostic scheme for bearing condition monitoring

IEEE Transactions on Instrumentation and Measurement

Volumn 59, Issue 2, 2010, Pages 309-321

  Liu, Jie ^a   Wang, Wilson ^b   Golnaraghi, Farid ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b LAKEHEAD UNIVERSITY   (Canada)

^c SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Bearing fault diagnostics; Machinery condition monitoring; Multistep prediction; Neurofuzzy (NF) schemes

Indexed keywords

BEARING CONDITION MONITORING; BEARING CONDITIONS; BEARING DEFECT; BEARING FAULT DIAGNOSTICS; ELECTRICAL FACILITIES; EXPERIMENTAL TEST; HEALTH CONDITION; MACHINERY CONDITION MONITORING; MACHINERY PERFORMANCE; MONITORING SYSTEM; MULTI-STEP; MULTI-STEP PREDICTION; NEURO-FUZZY;

BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); ELECTRIC LOAD FORECASTING; MACHINERY; SIGNAL PROCESSING;

CONDITION MONITORING;

EID: 76149141199     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2009.2023814     Document Type: Article

References (37)

1. B. A. Paya, I. I. Esat, and M. N. M. Badi, "Artificial neural-networkbased fault diagnostics of rotating machinery using wavelet transforms as a preprocessor," Mech. Syst. Signal Process., vol.11, no.5, pp. 751-765, Sep. 1997.
2. R. Isermann, "Supervision, fault-detection and fault-diagnosis methods- An introduction," Control Eng. Pract., vol.5, no.5, pp. 639-652, May 1997.
3. R. Patton, P. Frank, and R. Clark, Issues of Fault Diagnosis for Dynamic Systems. New York: Springer-Verlag, 2000.
4. R. Duda, P. Hart, and D. Stork, Pattern Classification. Hoboken, NJ: Wiley, 2001.
5. H. Chin, K. Danai, and D. Lewicki, "Fault detection of helicopter gearboxes using the multivalued influence matrix method," J. Mech. Des., vol. 117, no. 2A, pp. 248-253, Jun. 1995.
6. W. Wang and L. Song, "A multisupervision system for the quality control of a CNC system," J. Comput. Aided Eng., vol.18, no.3, pp. 27-35, 1996.
7. J. Bezdek and L. Kuncheva, "Nearest prototype classifier designs: An experimental study," Int. J. Intell. Syst., vol.16, no.12, pp. 1445-1473, Dec. 2001.
8. L. B. Jack, A. K. Nandi, and A. C. McCormick, "Diagnosis of rollingelement bearing faults using radial basis function networks," Appl. Signal Process., vol.6, pp. 25-32, 1999.
9. B. Samanta and K. R. Al-Balushi, "Artificial neural-network-based fault diagnostics of rolling-element bearings using time-domain features," Mech. Syst. Signal Process., vol.17, no.2, pp. 317-328, Mar. 2003.
10. R. Isermann, "On fuzzy logic applications for automatic control, supervision, and fault diagnosis," IEEE Trans. Syst., Man, Cybern. A: Syst., Humans, vol.28, no.2, pp. 221-235, Mar. 1998.
11. G. Castellano, A. Fanelli, and C. Mencar, "An empirical risk functional to improve learning in a neurofuzzy classifier," IEEE Trans. Syst., Man, Cybern. B: Cybern., vol.34, no.1, pp. 725-731, Feb. 2004.
12. J.Wang and C. Lee, "Self-adaptive neurofuzzy inference systems for classification applications," IEEE Trans. Fuzzy Syst., vol.10, no.6, pp. 790- 802, Dec. 2002.
13. O. Uluyol, K. Kim, and E. Nwadiogbu, "Synergistic use of soft computing technologies for fault detection in gas turbine engines," IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., vol.36, no.6, pp. 476-484, Jul. 2006.
14. W. Wang, "An intelligent system for machinery condition monitoring," IEEE Trans. Fuzzy Syst., vol.16, no.1, pp. 110-122, Feb. 2008.
15. W. Wang, "An enhanced diagnostic system for gear system monitoring," IEEE Trans. Syst., Man, Cybern. B: Cybern., vol.38, no.1, pp. 102-112, Feb. 2008.
16. P. Xu and A. K. Chan, "Fast and robust neural-network-based wheel bearing fault detection with optimal wavelet features," in Proc. Int. Joint Conf., 2002, vol.3, pp. 2076-2080.
17. W. Wang, F. Ismail, and F. Golnaraghi, "Assessment of gear damage monitoring techniques using vibration measurements," Mech. Syst. Signal Process., vol.15, no.5, pp. 905-922, Sep. 2001.
18. A. K. S. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mech. Syst. Signal Process., vol.20, no.7, pp. 1483-1510, Oct. 2006.
19. N. Tandon, "A comparison of some vibration parameters for the condition monitoring of rolling-element bearings," Measurement, vol.12, no.3, pp. 285-289, Jan. 1994.
20. R. B. W. Heng and M. J. M. Nor, "Statistical analysis of sound and vibration signals for monitoring rolling-element bearing condition," Appl. Acoust., vol.53, pp. 211-226, 1998.
21. R. B. Randall, J. Antoni, and S. Chobsaard, "The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals," Mech. Syst. Signal Process., vol.15, no.5, pp. 945-962, Sep. 2001.
22. J. R. Stack, T. G. Habetler, and R. G. Harley, "Fault-signature modeling and detection of inner-race bearing faults," IEEE Trans. Ind. Appl., vol.42, no.1, pp. 61-68, Jan./Feb. 2006.
23. Q. Du and S. Yang, "Improvement of the EMD method and applications in defect diagnosis of ball bearings," Meas. Sci. Technol., vol.17, no.8, pp. 2355-2361, Aug. 2006. (Pubitemid 44102283)
24. G. Y. Luo, D. Osypiw, and M. Irle, "Online vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing," J. Vib. Control, vol.9, no.8, pp. 931-947, 2003.
25. J. Liu, W. Wang, F. Golnaraghi, and K. Liu, "Wavelet spectrum analysis for bearing fault diagnostics," Meas. Sci. Technol., vol.19, no.1, pp. 1-9, Jan. 2008.
26. P. Estocq, F. Bolaers, J. P. Dron, and L. Rasolofondraibe, "Method of denoising by spectral subtraction applied to the detection of rolling bearing defects," J. Vib. Control, vol.12, no.2, pp. 197-211, 2006.
27. J. Antoni, "The spectral kurtosis: A useful tool for characterizing nonstationary signals," Mech. Syst. Signal Process., vol.20, no.2, pp. 282-307, Feb. 2006.
28. J. Vass, R. Smid, R. B. Randall, P. Sovka, C. Cristalli, and B. Torcianti, "Avoidance of speckle noise in laser vibrometry by the use of kurtosis ratio: Application to mechanical fault diagnostics," Mech. Syst. Signal Process., vol.22, no.3, pp. 647-671, Apr. 2008.
29. J. Altmann and J. Mathew, "Multiple bandpass autoregressive demodulation for rolling-element bearing fault diagnosis," Mech. Syst. Signal Process., vol.15, no.5, pp. 963-977, Sep. 2001.
30. J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy Soft Computing. Upper Saddle River, NJ: Prentice-Hall, 1997.
31. L. Ljung and T. Soderstrom, Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press, 1983.
32. Y. Chen, "A fuzzy decision system for fault classification under high levels of uncertainty," Trans. ASME, J. Dyn. Syst. Meas. Control, vol.117, no.1, pp. 108-115, Mar. 1995.
33. C. Lin and C. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall, 1996.
34. W. Wang, "An adaptive predictor for dynamic system forecasting," Mech. Syst. Signal Process., vol.21, no.2, pp. 809-823, Feb. 2007. (Pubitemid 44667411)
35. M. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol.197, no.4300, pp. 287-289, Jul. 1977.
36. L. Eren andM. J. Devaney, "Bearing damage detection via wavelet packet decomposition of the stator current," IEEE Trans. Instrum. Meas., vol.53, no.2, pp. 431-436, Apr. 2004.
37. C. Wang and R. X. Gao, "Wavelet transform with spectral postprocessing for enhanced feature extraction," IEEE Trans. Instrum. Meas., vol.52, no.4, pp. 1296-1301, Aug. 2003.