Particle filter-based prognostic approach for high-speed shaft bearing wind turbine progressive degradations

Track degradation of wind turbine high-speed shaft bearing can reduce unscheduled maintenance events, and safe power generation system. This paper proposes a particle filter-based prognostic approach for high-speed shaft bearing track degradation; this approach is validated by inspecting a real data from a wind turbine drivetrain. The particle filter-based prognostic results are compared with the standard support vector regression and Kalman smoother results. The particle filter method shows better results. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main improvement of the particle filter-based prognostic approach is its ability to produce a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

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