Bearings prognostic using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL estimation can be done by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data-driven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).