Wavelet Analysis based Gear Shaft Fault Detection
Volume 8, Number 3, May 2012 - Paper 2 - pp. 233-247
JING YU, VILIAM MAKIS and MING YANGDepartment of Mechanical and Industrial Engineering, University of Toronto,
5 King’s College Road, Toronto, ON Canada M5S 3G8
(Received on January 20, 2011 and revised on April 27, 2011 and Feb. 05, 2012)
Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, but there are very few papers dealing with the early detection of shaft cracks. In this paper, an approach to gear shaft fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. The autocovariance of maximal energy coefficients based on the wavelet transform is first proposed to evaluate the gear shaft fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using standard deviation, kurtosis and the application of the Kolmogorov-Smirnov test (K-S test), used as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above.
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