Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve remaining useful life prediction for bearings, three aspects should be studied: (1) degradation modeling; (2) inter stage change point identification; (3) degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing’s condition monitoring data to prevent false alarms. An adaptive incremental filtering is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16 004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.

This paper explores the cage whirl state under different operating conditions based on the cylindrical roller bearing dynamics model. The stability and whirl characteristics of the cage whirl are evaluated by considering the rotation and force of the cage and roller. The results demonstrate that the cage whirl state is divided into three types according to the cage whirl orbit: disordered whirl, local periodic whirl and regular circular whirl. With disordered whirl, the whirl speed and whirl radius of the cage are unpredictable, and the force of the cage changes frequently. With local periodic whirl, the rotation speed, whirl speed and the force on the cage change periodically or stay constant. In the case of a regular circular whirl, the force on the cage should be considered to evaluate the stability of the cage whirl. In addition, the collision force between the roller and cage determines whether the cage whirl speed is stable or not, and the bearing slipping has no obvious effect on cage whirl stability.

With the continuous advancement in bearing speed, bearing assembly clearance (pocket, radial, and guide clearances) has a particularly significant impact on dynamic characteristics of bearing. In order to improve its performance, it is necessary to generate an optimized design for assembly clearance based on dynamic analysis. In this paper, a dynamic model of cylindrical roller bearing has been put forth based on the variable step fourth-order Runge–Kutta method, while the simulation results have been verified by a high-speed bearing cage motion testing machine. Considering pocket, radial, and guide clearances as independent variables and maximum impact force, whirl deviation ratio, and minimum power loss as the objectives, a multiobjective optimized model of cylindrical roller bearing has been developed using central composite experimental design (CCD) and response surface method. After optimization, the maximum impact force was reduced by 9.26%, the whirl deviation ratio was reduced by 2.87%, and power loss was reduced by 1.45%.