Plastic deformation-based rolling bearing reliability and sensitivity analysis under incomplete probability information

It is currently difficult to adequately characterize the probability distribution information of rolling bearings, and research on the reliability of rolling bearings based on plastic deformation is lacking. To address this problem, the Latin hypercube design, BP neural networks, and higher-order moment method are combined, fully considering the randomness of the load, geometry, and material parameters of the rolling bearing. The dynamics model of the rolling bearing was constructed by using the Hertz contact theory and Jones model, combined with a Latin hypercube design to obtain neural network training samples. Based on the rational construction of the neural network structure, the mapping relationship between the equivalent stress and the design variables is obtained. The state function of the rolling bearing is constructed according to the stress-strength interference model, and then, the reliability index and reliability of the rolling bearing are obtained using the higher-order moment method, followed by a reliability sensitivity analysis. Finally, the proposed method is applied to the reliability analysis of a certain type of angular contact ball bearing, and the calculation results are compared with the Monte Carlo method results to demonstrate the correctness and effectiveness of the proposed method.