Dynamic Model Updating and Dynamic Response Prediction Method of RV Reducer Based on Hierarchical Bayesian Inference

As a core component of high-end equipment, the performance of RV reducer directly affects the performance of the whole machine. Dynamic analysis is an important way to improve the long-term stability of RV reducer service capacity. The current research work does not consider the influence of multi-source uncertainty factors when modeling the dynamic. This leads to a large error in the established theoretical model and affects the accuracy of the subsequent analysis. Therefore, the uncertainty quantification and propagation of RV reducer is studied based on hierarchical Bayesian inference to realize its dynamic model updating and dynamic response prediction. The aim is to provide an effective guarantee for the accurate assessment of the dynamic characteristics. According to the structure characteristics and transmission principle of RV reducer, the dynamic theoretical models of overall and the core components are established. The Newmark method is applied to solve the displacement, velocity and acceleration dynamic response of each component. By introducing a multi-layer hyperparameter probability distribution of the parameters to be updated, a hierarchical Bayesian probability model is constructed. The prior information of the parameters to be updated is fully considered, and the transition Markov chain Monte Carlo method is applied to obtain its posterior distribution. Thus, the RV reducer dynamics model is accurately updated. On this basis, the parameter uncertainty is transferred to realize the dynamic response prediction. For a domestic RV-20E reducer, the weak points, i.e., crank shaft and cycloid wheel, are analyzed by the proposed method. The posteriori distribution information of the stiffness parameters and the dynamic displacement response prediction results of both are obtained. © 2024 Chinese Mechanical Engineering Society. All rights reserved.