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 behavior 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.