One of the main reasons for failure in the wind turbine is the wear between the gear teeth during the power conversion and changes in the rotation speed, which is also generally associated with changes in the lubrication regimes. In this sense, vibration and signal analysis are frequently used in predictive maintenance as they usually permit the identification of deviations in the proper functioning of the equipment. Thus, this work aims to apply the continuous wavelet transform (CWT) to correlate gear wear and vibration signals, using visual and straightforward analysis. An experimental setup of a gear system was used to analyze vibration signals from different tooth gear damages. Gears with different levels and modes of damage were used in order to evaluate the sensitivity of vibration signals to them. The features from vibration signals were extracted by Morlet wavelet analysis. Results demonstrate that the proposed method accurately detected the early failure by visualization in frequency–time maps.
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