Gear fault detection using adaptive morphological gradient lifting wavelet

Wavelet transform is one of the most acceptable tools to analyze vibration signals for gear fault detection. However, there are still some limitations of the traditional wavelet transforms due to the utilization of fixed linear filters. This investigation presents an adaptive morphological gradient lifting wavelet (AMGLW) to remedy the shortcomings of traditional wavelet transform schemes. A novel nonlinear filter, named morphological gradient filter, is designed for enhancing the impulsive features of the original signal. Then the adaptability of AMGLW is implemented by selecting between two filters, namely the average filter and the morphological gradient filter, to update the approximation signal dependent upon the local gradient of the analyzed signal. This new scheme is evaluated on a simulated signal and a practical vibration signal measured from a gearbox. Experimental results demonstrate that the presented AMGLW outperforms the traditional linear wavelet (LW) transform obviously for detecting gear defects. Furthermore, the computational cost of AMGLW is much less than the traditional LW. Thus the AMGLW scheme is quite suitable for the online condition monitoring of gears.