Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition

Machinery condition monitoring is a key step to perform condition-based maintenance (CBM) policy. In this paper, a novel health evaluation method based on wavelet decomposition is proposed. In the process of wavelet decomposition, a new index is defined to choose the optimal detail signal. After that, frequency spectrum growth index (FSGI) is proposed to serve as a quantitative description of machine health condition. This index is helpful for maintenance decision-making. At the same time, a semi-dynamic threshold criterion that can be used to check the existence of fault is established. In order to demonstrate the performance of this index with its semi-dynamic threshold, a comprehensive study with three sets of vibration data collected from a mechanical diagnostics test bed is conducted to validate this method. The analysis results indicate that the proposed method is insensitive to the selection of wavelet function and wavelet decomposition level, which means that FSGI has excellent performance in gear early fault detection.

[1]  A. Mohanty,et al.  APPLICATION OF DISCRETE WAVELET TRANSFORM FOR DETECTION OF BALL BEARING RACE FAULTS , 2002 .

[2]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[3]  P. D. McFadden,et al.  Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration , 1986 .

[4]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[5]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[6]  Daming Lin,et al.  An approach to signal processing and condition-based maintenance for gearboxes subject to tooth failure , 2004 .

[7]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[9]  Qiang Miao,et al.  Singularity detection in machinery health monitoring using Lipschitz exponent function , 2007 .

[10]  Richard Buessow,et al.  An algorithm for the continuous Morlet wavelet transform , 2007, 0706.0099.

[11]  Tet Hin Yeap,et al.  A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings , 2007 .

[12]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .