Research on the Fault Diagnosis of Planetary Gearbox

As a main transmission composition, planetary gearbox is widely used in wind turbine generation system. Due to the complicated working environment, sun gears, planet gear, ring gear and other key components are prone to failure. Therefore, the researches on the fault features of the planetary gearbox have significance to understand the operation of wind turbines, timely discover fault position and predict the trend of running status. In this paper, a method is proposed for the fault diagnosis of the planetary gearbox combined maximum correlation kurtosis deconvolution and frequency slice wavelet transform. The maximum correlation kurtosis deconvolution with particle swarm optimization algorithm is used to improve the signal to noise ratio. The frequency slice wavelet transform transferred the denoised signal into time-frequency domain to identify the gear fault. The feasibility of the proposed method is verified by testing the experimental signal through the test rig.

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