Gear fault diagnosis based on continuous wavelet transform

A new approach of gear fault diagnosis based on continuous wavelet transform is presented. Continuous wavelet transform can provide a finer scale resolution than orthogonal wavelet transform. It is more suitable for extracting mechanical fault information. In this paper, the concept of time-averaged wavelet spectrum (TAWS) based on Morlet continuous wavelet transform is proposed. Two fault diagnosis methods named spectrum comparison method (SCM) and feature energy method (FEM) based on TAWS are established. The results of the application to gearbox gear fault diagnosis show that TAWS can effectively extract gear fault information. The feature energy of the TAWS features the gear fault advancement very well and is conically proportional to the gear fault advancement. (C) 2002 Elsevier Science Ltd. All rights reserved.