Wind turbine high-speed shaft bearing degradation analysis for run-to-failure testing using spectral kurtosis

Abstract

Premature failures of wind turbine gearboxes increase the price of energy and affect their reliability. Most gearbox failures initiate in bearings. High-speed bearings and planetary bearings exhibit a high rate of premature failure. A critical work of bearing fault diagnosis is finding the optimum frequency band that covers faulty bearing signal, which is a challenging task in practice. The kurtogram is a high technic used to characterize non-stationarities hidden in a signal. Thus, allows responding to the given problem. It consists to determine the central frequency (resonance) and the appropriate bandwidth witch maximizes the kurtosis. This paper addresses a squared envelope based spectral kurtosis method diagnosis for skidding in high-speed shaft bearings. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real measured data from a drive train wind turbine.