Abstract:Rolling bearings have different degrees of degradation in performance during longterm work. If the degraded state of the rolling bearing can be identified, maintenance measures can be taken. Aiming at the performance degradation evaluation of rolling bearings, a method for evaluating the degradation of rolling bearing performance is proposed, which combines the vibration signal autoregressive model (AR) energy ratio and the support vector data description (SVDD). Firstly, the residual components of the vibration signal in the whole life cycle of the bearing are obtained by AR filtering, and the energy ratio is calculated as the feature vector of the bearing state. Then, SVDD is trained by using the feature vector of the bearing under normal state, and the hypersphere under normal state is obtained. The relative distance between the feature vector of bearing life cycle sample and the hypersphere is used as the quantitative evaluation index of bearing performance degradation. Finally, the early fault alarm threshold is set to determine the early fault point. The results show that compared with the performance degradation assessment methods of common monitoring indicators, the early fault detection capability of the proposed method is stronger, and the description of each stage of bearing performance degradation is more accurate.