Particle Filter Based Approach for Remaining Useful Life Prediction of High-Speed Shaft Bearing in Wind Turbine Generators

This work involves a novel data-driven procedure using vibration analysis for bearing health prognosis. In this work, we investigate the time-domain features and applying spectral kurtosis features in order to extract the damage indicators which eventually represent the degradation of the high-speed shaft bearing (HSSB). These damages were characterized by their Monotonicity, Trendability, and Prognosability. The most appropriate indicator was then used as a health index for the remaining useful life (RUL) prediction task. In this study, we used an integrated approach based on Particle Filter approach which was then developed for direct RUL prediction of HSSB. This methodology was validated using real world vibration data wind turbine gearbox. The experimental results and the prognostics metrics like fitness degree equal to 0.9941 shown that the Particle Filter approach is more feasible prediction tool.