A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines
Wei Teng, Han Chen, Yankang Hu, Xin Cheng, Lei Song, Yibing Liu
Abstract
Accurate remaining useful life prognosis of bearings in wind turbines can effectively help to schedule maintenance strategy and reduce operational costs at wind farms. Unscented particle filter is good at state tracking in nonlinear problem. A robust model-based approach based on improved unscented particle filter is presented to deal with bearing life prognosis in wind turbines, which involves: (1) The mean of sigma points after unscented Kalman transform is regarded as the particles in particle filter to guarantee the particles aggregation; (2) Several past measurements are utilized to estimate the likelihood function of current step; (3) Uniform distribution is adopted for resampling particles to make them diversity. The presented remaining useful life prognosis approach depends more on the measurement, rather than the initial parameters of degradation model, which makes it practicable for the on-site wind turbines. Three life-cycle bearings from wind turbine high-speed shafts demonstrate the effectiveness of the proposed approach.