To address the challenges of machine tool reliability modeling and the lack of reliability data， a reliability assessment method integrating lifetime information with multi-performance degradation information is proposed， starting from the basic motion units. Using the function-motion-action （FMA） decomposition method， the machine tool is broken down into its smallest motion units， i.e.， the meta-action units. The reliability level of the machine tool is ensured by accurately assessing the reliability of the key elemental action units. A non-linear Wiener process model under random effects is developed to capture individual variations， shared characteristics， and non-linearity in degradation processes. To address limited degradation data， a reliability model incorporating failure life information is established using Bayesian methods. Finally， considering correlations among multiple performance degradation failures， a comprehensive reliability assessment model is formulated using the Copula function， with parameter estimation achieved via the MCMC-Gibbs sampling algorithm. Focusing on the worm gear rotation meta-action unit of a specific computerized numerical control gear hobbing machine， case studies and comparative analysis highlight the method’s feasibility and superiority.