Machine degradation prognostic based on RVM and ARMA/GARCH model for bearing fault simulated data

Recently, prognostics is an active area and growth rapidly. In this paper, bearing prognostic has been studied in viewpoint of failure degradation as an object of prediction. This study proposes the application of relevance vector machine (RVM), logistic regression (LR) and ARMA/GARCH in order to assess the failure degradation of run-to-failure bearing simulated data. Failure degradation is calculated using LR and then regarded as target vectors of failure probability for RVM training. ARMA/GARCH based on multi-step-ahead prediction is employed for censored data. Furthermore, RVM is selected as intelligent system then trained by using run-to-failure bearing data and target vectors of failure probability estimated by LR. After training process, RVM is employed to predict failure probability of individual unit of bearing sample. The result shows the novelty of the proposed method which can be considered as machine degradation prognostic model.