Evaluation of the Training Process of three different Prognostic Approaches based on the Gaussian Process

TL;DRAbstract

Data-driven prognostic approaches like Gaussian Process combined with Unscented Kalman Filter (GPUKF) are promising methods for predicting the Remaining Useful Lifetime (RUL) of a degrading component. Whereas the Gaussian Process (GP) is appropriate to derive a suitable degradation model by means of a set of training data, the Unscented Kalman Filter (UKF) employs this model to determine the prediction and its uncertainty.Since a degradation process is highly stochastic, it is assumed that by applying more sets of training data the accuracy and precision of the GPUKF is increased. In order to examine the performance enhancement two different approaches are investigated in this paper: First, a single GP is trained with all available data sets. The second approach combines several GPs (each created with a data set of one degradation process) by extending the GPUKF with a Multiple Model Method. The development of a third prognostic approach aims at the investigation of the UKF as a suitab

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