A new SKRgram based demodulation technique for planet bearing fault detection

Abstract Planet bearing fault detection is one of the most challenging issues in planetary gearbox condition monitoring. The intricate structure of a planetary gearbox will fail traditional bearing fault diagnosis algorithms by bringing in strong and complex planetary gear noise. In specific, planetary gear noise with multi-sidebands and high magnitude will not only fail the former gear noise elimination algorithms but also affect the methods designed for highlighting bearing-fault-related content. As such, we propose a new approach with four main steps to address this issue: a) calculate the spectral kurtosis (SK) matrix of a healthy planetary gearbox as baseline, b) obtain SKRgram (Spectral kurtosis ratio gram) by calculating the ratio between SK matrix of raw signal and the baseline, c) locate potential filtering areas from the SKRgram using SKR value as criterion and then select potential optimal filter bands among them with the standard of kurtosis value, d) highlight the faulty planet bearing content by filtering the raw signal through potential filter bands and identify the fault type of planet bearing by comparing the filtered results with the fault envelope pattern. The accuracy and effectiveness of the proposed planet bearing fault detection algorithm are verified by both the simulated and experimental data.

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