Remaining useful life estimation based on nonlinear feature reduction and support vector regression

Prognostics and Health Management (PHM) of rotating machines is gaining importance in industry and allows increasing reliability and decreasing machines’ breakdowns. Bearings are one of the most components present in mechanical equipments and one of their most common failures. So, to assess machines’ degradations, fault prognostics of bearings is developed in this paper. The proposed method relies on two steps (an offline step and an online step) to track the health state and predict the remaining useful life (RUL) of the bearings. The offline step is used to learn the degradation models of the bearings whereas the online step uses these models to assess the current health state of the bearings and predict their RUL. During the offline step, vibration signals acquired on the bearings are processed to extract features, which are then exploited to learn models that represent the evolution of the degradations. For this purpose, the isometric feature mapping reduction technique (ISOMAP) and support vector regression (SVR) are used. The method is applied on a laboratory experimental degradations related to bearings. The obtained results show that the method can effectively model the evolution of the degradations and predict the RUL of the bearings.