20. The enhancement of bearing fault detection using narrowband interference cancellation
Xinghui Zhang1, Jianshe Kang2, Eric Bechhoefer3, Hongzhi Teng4
1, 2Mechanical Engineering College, Shijiazhuang, 050003, China
3GPMS LLC, Cornwall, VT, USA
4Maintenance Center, Lanzhou, 730060, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
(Received 30 April 2013; accepted 7 September 2013)
Abstract. Bearings play an important role in mechanical transmission. Many disasters are due to bearing faults. This has driven the need in research for early bearing fault detection. The goal is to extract the periodic impulse signals from the very noise signal which are indicative of a bearing fault. This is done by enhancing impulsive signals while suppressing other signals. This paper used a new method, Narrowband Interference Cancellation, to detect incipient bearing fault. This method filters the narrowband signal not associated with the impulsive signal produced by bearing fault. This improves the signal to noise ratio of impulse train associated with the bearing fault frequency. Finally this methodology is demonstrated on a bearing outer race fault.
Keywords: incipient fault detection, bearing, narrowband interference cancellation.
 Antoni J., Randall R. B. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, Vol. 20, Issue 2, 2006, p. 308‑331.
 Bechhoefer E., Kingsley M., Menon P. Bearing envelope analysis window selection using spectral kurtosis techniques. IEEE Conference on Prognostics and Health Management, Montreal, 2011.
 Eftekharnejad B., Carrasco M. R., Charnley B., Mba D. The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mechanical Systems and Signal Processing, Vol. 25, Issue 1, 2011, p. 266‑284.
 Zhu K. S., Song X. G., Xue D. X. Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient. Journal of Vibroengineering, Vol. 15, Issue 2, 2013, p. 597‑609.
 Jiang K. S., Xu G. H., Liang L., Zhao G. Q., Tao T. F. A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering. Journal of Vibroengineering, Vol. 14, Issue 4, 2012, p. 1862‑1875.
 Moosavian A., Ahmadi H., Tabatabaeefar A. Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine. Journal of Vibroengineering, Vol. 14, Issue 2, 2012, p. 894‑906.
 Wang W., Wong A. K. Autogressive model-based gear fault diagnosis. ASME Transactions, Journal of Vibration and Acoustics, Vol. 124, Issue 2, 2002, p. 172‑179.
 Sawalhi N., Randall R. B., Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, Vol. 21, Issue 6, 2007, p. 2616‑2633.
 Chaturvedi G. K., Thomas D. W. Bearing fault detection using adaptive noise cancelling. Journal of Sound and Vibration, Vol. 104, Issue 2, 1982, p. 280‑289.
 Tan C. C., Dawson B. An adaptive noise cancellation approach for condition monitoring of gearbox bearings. Proceedings of the International Tribology Conference, Melbourne, 1987.
 Ho D. Bearing Diagnostics and Self Adaptive Noise Cancellation. Ph.D. Dissertation, UNSW, 2000.
 Antoni J., Randall R. B. Unsupervised noise cancellation for vibration signals: Part I – Evaluation of adaptive algorithms. Mechanical Systems and Signal Processing, Vol. 18, Issue 1, 2004, p. 89‑101.
 Antoni J., Randall R. B. Unsupervised noise cancellation for vibration signals: Part II – A novel frequency-domain algorithm. Mechanical Systems and Signal Processing, Vol. 18, Issue 1, 2004, p. 103‑117.
 Klein R., Rudyk E., Masad E., Issacharoff M. Emphasizing bearing tones for prognostics. The International Journal of Condition Monitoring, Vol. 1, Issue 2, 2011, p. 73‑78.
 Klein R., Rudyk E., Masad E., Issacharoff M. Model based approach for identification of gears and bearings failure modes. International Journal of Prognostics and Health Management, Vol. 2, Issue 2, 2011, p. 9.
 Bechhoefer E., Li R. Y., He D. Quantification of condition indicator performance on a split torque gearbox. Journal of Intelligent Manufacturing, Vol. 23, Issue 2, 2012, p. 213‑220.
 Manolakis D., Ingle V., Kogon S. Statistical and Adaptive Signal Processing. New York, McGraw-Hill, 2000.
 Fault Data Sets, http://www.mfpt.org/.
Cite this article
Zhang Xinghui, Kang Jianshe, Bechhoefer Eric, Teng Hongzhi The enhancement of bearing fault detection using narrowband interference cancellation. Journal of Measurements in Engineering, Vol. 1, Issue 3, 2013, p. 130‑136.
Journal of Measurements in
Engineering. September 2013, Volume 1, Issue 3