Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model

As the central component of rotating machine, the performance reliability assessment and remaining useful lifetime prediction of bearing are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. A prognostic algorithm to assess the reliability and forecast the remaining useful lifetime (RUL) of bearings was proposed, consisting of three phases. Online vibration and temperature signals of bearings in normal state were measured during the manufacturing process and the most useful time-dependent features of vibration signals were extracted based on correlation analysis (feature selection step). Time series analysis based on neural network, as an identification model, was used to predict the features of bearing vibration signals at any horizons (feature prediction step). Furthermore, according to the features, degradation factor was defined. The proportional hazard model was generated to estimate the survival function and forecast the RUL of the bearing (RUL prediction step). The positive results show that the plausibility and effectiveness of the proposed approach can facilitate bearing reliability estimation and RUL prediction.