A new remaining useful life (RUL) predictive model for machine parts is proposed

which combines support vector machine (SVM) and non-linear Kalman filter. The proposed model is expected to fix the awkward situation in that the degradation data from both database and the predicted part cannot be used at the same time by most of the exiting RUL models. The SVM regression model trained by data from full-life tests is treated as the status update equation of non-linear Kalman filter. The time update equation is constructed according to the degradation characteristics of machine parts. RUL estimations and corresponding confidence intervals of point-in-time are computed iteratively after setting the initial RUL value and its variance. The proposed model makes the best use of data from both full-life tests of the same or similar parts and current part during its degradation. The accuracy of RUL estimation

the stability and practical values of the proposed model are illustrated by analyzing a certain type of antifriction bearings. Key