Early fault diagnosis of gearbox using Empirical Wavelet Transform and Hilbert Transform

Abstract

Gears are one of the most common mechanisms used for transmitting power and motion in various mechanical applications. Tooth pitting fault is frequently failure modes encountered. An analytical model of one stage spur gearbox is presented where the effects of tooth pitting fault were simulated by magnitude and phase changes in the gearmesh stiffness. This paper deals with the problem by using the Empirical Wavelet Transform (EWT) and the Hilbert Transform (HT) techniques. First, the EWT is used to extract adaptive modes from the vibration signals by designing an appropriate wavelet Alter bank. Then, the instantaneous frequencies are performed for each mode using the HT. The proposed tooth pitting fault diagnosis method was tested on both clean and noisy signals to evaluate its performance. The results show that the proposed method can effectively detect the fault in an early stage of development.