CLUTCH SYSTEM REMAINING USEFUL LIFE PREDICTION USING WEAR AND DYNAMIC MODELING EFFECTS

摘要

A model-based technique is presented for Remaining Useful Life (RUL) prediction of highly dynamic, high power dry clutch systems that combines physics-based simulation and wear prediction models. Primary load and engagement shear drivers (i.e., torque, speed, clutch surface temperature) are modeled using a first principle approach. An extension of Archard's law is presented in which life usage is predicted using multiple stochastic models to determine a wear coefficient for each applicable wear mechanism. The models consider the physical wear process, including debris particle and protective layer formation, using parameters such as surface roughness, particle size, and surface temperature. These stochastic variables are evaluated in a probabilistic framework using statistical methods, such as Monte Carlo Simulation and importance sampling, that consider both measurement and modeling uncertainty. Confidence interval prognostic results are provided to predict the RUL of the clutch throughout its limited life in near-real time.