A novel health estimation model for CNC machine tool servo system based on belief-rule-base
Health condition estimation is an important means to guarantee the normal work and the optimal maintenance for CNC machine tool servo system. The health condition is denoted by features. In order to estimate the health condition for servo system, belief-rule-base (BRB) model is employed as estimation method in this paper. This method can composite multiple knowledge including some uncertain information to improve the accuracy of health condition estimation. Because of the multiplicity of the feature parameters, infinite irrelevance method is used to extract main characteristics. As the initial knowledge by experts is not accurate enough, CMA-ES is used to optimize the parameters in BRB. The case study show that the model can estimate the health of servo system accurately.