A Transient Enhancement Method for Two-Stage Helicopter Gearbox Fault Diagnosis Based on ALE


Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not been detected successfully in author’s previous studies.

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X. Tian, T. Wang, Z. Chen, F. Gu and A. Ball, "A Transient Enhancement Method for Two-Stage Helicopter Gearbox Fault Diagnosis Based on ALE," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 132-137. doi: 10.4236/jsip.2013.43B023.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. M. Ramli, A. O. A. Noor and S. A. Samad, “A Review of Adaptive Line Enhancers for Noise Cancellation,” Australian Journal of Basic and Applied Sciences, Vol. 6, No. 6, 2012, pp. 337-352.
[2] S. K. Lee and P. R. White, “The Enhancement of Impulsive Noise and Vibration Signals for Fault Detection in Rotating and Reciprocating Machinery,” Journal of Sound and Vibration, Vol. 217, No. 3, 1998, pp. 485-505. doi:10.1006/jsvi.1998.1767
[3] J. R. Treichler, “Transient and Convergent Behaviour of the Adaptive Line Enhancer,” IEEE Trans. Acousr. Speech Signal Processing. Vol. ASSP-27, pp. 53- 62.
[4] N. Sasaoka, K. Sumi, Y. Itoh and K. Fujii, “A New Noise Reduction System Based on ALE and Noise Reconstruction Filter,” In pro-ceeding of: International Symposium on Circuits and Systems, Kobe, 23-26 May 2005.
[5] J. R. Mohammed, M. S. Shafi, S. Imtiaz etc. “An Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters,” International Journal of Electrical and Computer Engineering, Vol. 2, No. 3, pp. 325-332.
[6] B. Widrow, J. R. Glover Jr, J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Zeidler, D. Eugene, Jr. and R. C. Goodlin, “Adaptive Noise Cancelling: Principles and Applications,” Proceedings of the IEEE, Vol. 63, No. 12, pp. 1692-1716.
[7] S. Haykin, “Adaptive Filter Theory,” Prentice Hall, 2002.
[8] S. R. Diniz, P., 2008. Adaptive Filtering: Algorithms and Practical Implementation, Springer.
[9] W. T. Wu, J. Lin, S. B. Han and X. H. Ding, “Time domain averaging based on fractional delay filter,” Mechanical Systems and Signal Processing, Volume 23, No. 5, 2009, pp. 1447-1457. doi:10.1016/j.ymssp.2009.01.017
[10] J. R. Mohammed, “A New Simple Adaptive Noise Cancellation Scheme Based on ALE and NLMS Filter,” Annual Conf. Communication, Networks, Services Research, May 2007, pp. 245-254.

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