This invention discloses a method for maintaining the service performance of
machine tool feed systems based on
sensitivity analysis using a twin model and a
particle swarm optimization algorithm. The overall method encompasses a five-layer framework for digital twin modeling, electromechanical wear mechanism fusion and encapsulation, time-varying element
sensitivity analysis, and multi-objective maintenance decision optimization. The modeling part employs an object-oriented five-layer architecture, deeply encapsulating physical mechanisms such as
electromechanical coupling wear evolution based on spatial location distribution into a quasi-
physical model layer, achieving accurate description and real-time updates of implicit time-varying elements such as localized guideway wear. The decision-making part uses the Sobol
sensitivity analysis method to quantify the
impact weight of each element on service performance, constructs a full-effect exponential-driven adaptive tightening mechanism for early warning thresholds, and combines it with a
particle swarm optimization algorithm (PI-PSO) that introduces degradation
inertia and physical boundary penalties to iteratively calculate the
optimal maintenance scheme with the goal of minimizing maintenance costs,
downtime losses, and performance degradation. This method not only solves the problem that general models are difficult to describe the underlying electromechanical-
dynamic coupling mechanism but also achieves
deep integration of optimization algorithms and physical degradation laws, significantly improving the service reliability and
economic benefits of
machine tool feed systems.