Journal of Intelligent Manufacturing

, Volume 23, Issue 2, pp 313–321 | Cite as

Fault features extraction for bearing prognostics

Article

Abstract

Over the past years, investigation on condition-based maintenance (CBM) technique on bearing has been conducted. Bearing diagnostics and prognostics are the important aspects in CBM. A key to the success of using vibration data for bearing fault diagnostics and bearing lifecycle prognostics is a quantified relationship between bearing damage and bearing fault features. To establish such a quantitative relationship, effective signal processing techniques to extract bearing fault features from vibration signals are needed. This paper describes a newly developed fault feature extraction method for bearing prognostics. The effectiveness of the method is demonstrated with two real bearing run-to-failure test datasets: one collected under normal operating conditions and another one under abnormal operating conditions. Experimental results show that the bearing fault features extracted using both traditional vibration analysis methods and the proposed method give clear bearing heath degradation trend for the dataset collected under normal operating conditions. However, for the data collected under abnormal operating conditions, bearing fault features obtained using traditional vibration analysis methods fail to show the bearing health degradation trend while the fault features extracted using the proposed method give consistent bearing degradation trends.

Keywords

Bearing prognostics Condition-based maintenance Fault feature extraction Run-to-failure test 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gebraeel N., Lawley M., Liu R., Parmeshwaran V. (2004) Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics 51: 694–700CrossRefGoogle Scholar
  2. He, D., & Bechhoefer, E. (2008a). Development and Validation of Bearing Diagnostic and Prognostic Tools using HUMS Condition Indicators. Proceedings of 2008 IEEE Aerospace Conference, Big Sky, MT.Google Scholar
  3. He, D. & Bechhoefer, E., (2008b). Bearing prognostics using HUMS condition indicators, Proceedings of the 2008 AHS Forum, Montreal, Canada.Google Scholar
  4. Heng R., Nor M. J. M. (1998) Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics 53: 211–226CrossRefGoogle Scholar
  5. Huang R., Xi L., Li X., Liu C., Qiu H., Lee J. (2007) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 21: 193–207CrossRefGoogle Scholar
  6. IEEE Motor ReliabilityWorking Group. (1986). Report of large motorreliability survey of industrial and commercial installations. IEEE Transactions on Industry Applications (IA-21), 4, 853–872.Google Scholar
  7. Jardine A. K. S., Lin D., Banjevic D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20: 1483–1510CrossRefGoogle Scholar
  8. Li, R., He, D., & Bechhoefer, E. (2009). On quantification of bearing damage for lifecycle prognostics. Proceedings of AHS 65 2009 Conference, Dallas, TA.Google Scholar
  9. Liu, Y., & He, D. (2008). Damage mechanics based bearing prognosis using HUMS condition indicators. Proceedings of 2008 MFPT Conference, Virginia Beach, VA.Google Scholar
  10. Ljung, L. (1987). System identification: Theory for the user. Prentice Hall Information and System Sciences Seriers.Google Scholar
  11. McFadden P.D., Smith J.D. (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribology International 17: 3–10CrossRefGoogle Scholar
  12. McInerny S. A., Dai Y. (2003) Basic vibration signal processing for bearing fault detection. IEEE Transactions on Education 46(1): 149–156CrossRefGoogle Scholar
  13. Najjar B. A. L. (2000) Accuracy, effectiveness and improvement of vibration-based maintenance in paper mills: Case studies. Journal of Sound and Vibration 229(2): 389–410CrossRefGoogle Scholar
  14. Prabhakar S., Mohanty A. R., Sekhar A. S. (2002) Application of discrete wavelet transform for detection of ball bearing race faults. Tribology International 35: 793–800CrossRefGoogle Scholar
  15. Raheja D., Llinas J., Nagi R., Romanowski C. (2006) Data fusion/data mining-based architecture for condition-based maintenance. International Journal of Production Research 44(14): 2869–2887CrossRefGoogle Scholar
  16. Sawalhi N., Randall R. B., Endo H. (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing 21: 2616–2633CrossRefGoogle Scholar
  17. Tse P., Peng Y., Yam R. (2001) Wavelet analysis and envelope detection for rolling element bearing fault diagnosis their effectiveness and flexibilities. Journal of Vibration and Acoustics 123: 303–310CrossRefGoogle Scholar
  18. Wang W., Wong A. (2002) Autoregressive model-based gear fault diagnosis. Journal of Vibration and Acoustics 124: 172–179CrossRefGoogle Scholar
  19. Yan R., Gao R. X. (2004) Complexity as a measure for machine health evaluation. IEEE Transactions on Instrumentation and Measurement 53: 1327–1334CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Mechanical & Industrial EngineeringThe University of Illinois at ChicagoChicagoUSA

Personalised recommendations