Real-Time Life Prediction for Rolling Bearings Based on Nonparametric Bayesian Updating Method

Real-time life prediction for rolling bearings contributes to maintenance decision-making and optimization based on the health state. Real-time life prediction based on Bayesian methods usually require that the priori distribution of the product be obtained; however, this task is extremely difficult to implement for new products or small sample sizes. To solve this problem, a nonparametric Bayesian updating method is proposed in this study. Kernel density estimation is employed to estimate the priori and posterior distribution of parameters by integrating real-time performance degradation information. Thus, bearing real-time life prediction based on nonparametric Bayesian updating is realized. In addition, this study investigates the calculation and normalization process of the working condition conversion factor. The effectiveness of the proposed method is verified by bearing run-to-failure experiments.