Application of Multiwavelet Adaptive Threshold Denoising in the Fault Diagnosis of Gearbox

Abstract

In order to extract the weak fault information submerged in strong background noise of the gearbox vibration signal, multiwavelet denoising method with adaptive threshold and envelope demodulation method are applied in this paper. Multiwavelets have many excellent properties that single wavelet can not satisfy simultaneously, such as symmetry, orthogonality, compact support and high vanishing moments etc, which make it can match different characteristics of analyzed signal, because it contains several scaling functions and wavelet functions. GHM multiwavelet and db2 wavelet are used respectively to analyze the experimental signal with outer race fault of rolling bearings, in which adaptive threshold selection strategy is introduced in multiwavelet denoising. Based on the comparison of denoising effects, the conclusion can be drawn that multiwavelet adaptive threshold denoising is much more effective than single wavelet. Furthermore, multiwavelet denoising method is applied on engineering data. The results have shown that this kind of denoising method can identify the incipient fault feature as early as possible, which can't be realized by single wavelet.