Small sample reliability modeling of CNC machine tools based on Bayes theory

For some long-life subsystems of CNC machine tools, the field test fault data is often less. Modeling methods for small sample data are currently a hot research topic. In view of the fact that the fault data of the machining center subsystem is small sample data, the small sample parameter estimation method based on Bayes theory is used to study the subsystem reliability modeling. The parameter estimation of Weibull distribution is optimized by differential evolution algorithm, and the reliability model of each subsystem is established. Bayesian theory applied to small sample data can integrate prior information and sample information. The differential evolution algorithm is combined with Bayes theory to further improve the efficiency and accuracy of small sample data modeling. Through the reliability evaluation results, it can be found that the spindle box has the lowest MTBF and the lowest reliability level. The conclusion obtained lays the foundation for targeted measures to increase the reliability of machining centers.