Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling

Abstract

Vibration signals from any dynamic system are measured with respect to time. Different options are available to analyze these measured signals due to the advancement in computing and signal processing techniques. In the present work, a methodology comprising of discrete wavelet transform and autoregressive model has been proposed for detection of single tooth fault in single stage reduction bevel gearbox. An autoregressive model is constructed using detailed coefficients of discrete wavelet transform to highlight the presence of the fault in gearbox. The results show that variance of autoregressive coefficients obtained for faulty signal is more than the variance of autoregressive coefficients extracted from healthy signal. Based on the results, it is concluded that the proposed methodology can be used as health condition indicator of the gearbox system.