Metallic elastic elements serve as critical structural components in advanced electro mechanical assemblies, where cyclic loading induces progressive rigidity reduction. Within electro-hydraulic servo valves (EHSV), the feedback mechanism constitutes an essential closed-loop control element whose mechanical property deterioration directly compromises system precision and operational stability. A predictive methodology is introduced for estimating component lifespan under dynamic loading conditions through integrated System Performance Modeling (SPM) and Stiffness Degradation Prediction (SDP) frameworks. Given physical constraints precluding direct rigidity assessment, this approach leverages externally observable parameters for indirect condition monitoring while SPM supplies necessary loading profiles to SDP. The SDP framework integrates continuum damage mechanics principles with machine learning algorithms, with its output continuously refining SPM structural parameters. Through synchronized simulation protocols, this coupled methodology quantifies component rigidity evolution and evaluates system performance metrics to determine remaining operational lifespan. This integrated approach establishes a foundation for predictive maintenance in precision hydraulic control systems.
Pan et al. (Mon,) studied this question.