The degradation of rolling-element bearings is mainly stochastic due to unforeseeable influences like short term overstraining, which hampers the prediction of the remaining useful lifetime. This stochastic behaviour is hardly describable with parametric degradation models, as it has been done in the past. Therefore, the two prognostic concepts presented and examined in this paper introduce a nonparametric approach by the application of a dynamic Gaussian Process (GP). The GP offers the opportunity to reproduce a damage course according to a set of training data and thereby also estimates the uncertainties of this approach by means of the GP’s covariance. The training data is generated by a stochastic degradation model that simulates the aforementioned highly stochastic degradation of a bearing fault. For prediction and state estimation of the feature, the trained dynamic GP is combined with the Unscented Kalman Filter (UKF) and evaluated
in the context of a case study. Since this prognostic approach has shown drawbacks during the evaluation, a multiple model approach based on GP-UKF is introduced and evaluated. It is shown that this combination offers an increased prognostic performance for bearing fault prediction.
How to Cite
remaining useful life (RUL), Data-driven and model-based prognostics, Gaussian Process Model, Bearing Faults, Multiple Model
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