Incipient Faults Identification in Gearbox by Combining Kurtogram and Independent Component Analysis

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Abstract:

Envelope analysis is a popular incipient fault identification tool for rolling element bearings (REBs) and gears. However, in some harsh conditions where more than one fault of REBs and gears exists simultaneously in a gearbox. In general, only the characteristic frequencies of the stronger vibration can be exposed clearly, and the others may be missed by conventional envelope analysis. To address this issue, an incipient faults detection scheme combining the kurtogram and independent component analysis (ICA) for gearbox faults diagnosis is proposed in this paper. In the proposed scheme, multi-channel vibrations are acquired from the gearbox synchronously at first. Subsequently, the vibration envelopes from each channel are extracted by the novel fast kurtogram algorithm. Then, the independent component analysis algorithm is utilized to separate the envelopes. As a result, the independent envelope components corresponding to different sources are obtained. Finally, the characteristic frequencies of the incipient faults of rolling element bearings and/or gears in a gearbox can be clearly exposed in envelope spectral plots. An experiment on a gearbox test rig which has both a REB fault and a gear fault is conducted to compare the conventional envelope analysis scheme and the proposed scheme. Test results show that the proposed scheme is more effective to identify the incipient faults of REBs and gears simultaneously existing in a gearbox.

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309-313

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May 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] P.D. McFadden and J.D. Smith: Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review, Tribology International Vol. 17 (1984), p.3.

DOI: 10.1016/0301-679x(84)90076-8

Google Scholar

[2] J. Antoni: Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems and Signal Processing Vol. 21 (2007), p.108.

DOI: 10.1016/j.ymssp.2005.12.002

Google Scholar

[3] A. Hyvärinen, J. Karhunen and E. Oja, in: Independent Component Analysis, edited by Simon Haykin, John Wiley & Sons, Inc, NY (2001).

Google Scholar

[4] D. Ho and R.B. Randall: Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing Vol. 14 (2000), p.763.

DOI: 10.1006/mssp.2000.1304

Google Scholar

[5] N.G. Nikolaou and I.A. Antoniadis: Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets, Mechanical Systems and Signal Processing Vol. 16 (2002), p.677.

DOI: 10.1006/mssp.2001.1459

Google Scholar

[6] J. Antoni and R.B. Randall: Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm. Mechanical Systems and Signal Processing Vol. 18 (2004), p.103.

DOI: 10.1016/s0888-3270(03)00013-x

Google Scholar

[7] R.B. Randall and N. Sawalhi: A new method for separating discrete components from a signal, Sound and Vibration Vol. 45 (2011), p.6.

Google Scholar

[8] R.B. Randall and J. Antoni: Rolling element bearing diagnostics—A tutorial, Mechanical Systems and Signal Processing Vol. 25 (2011), p.485.

DOI: 10.1016/j.ymssp.2010.07.017

Google Scholar