Wear Stage Judgment and Wear Failure Prediction Based on Dissipative Theory of Wear

Assessing the failure of friction pairs is a crucial approach to prevent accidents. Researchers have endeavored to forewarn potential accidents through monitoring wear and friction coefficient. However, these parameters indicate abrupt changes only after the failure of the friction pair has already occurred. Thus, it is imperative to develop a wear prediction method that can anticipate the wear and friction process. This study investigates the variation of the system energy during friction and wear and proposes a criterion for categorizing the wear process based on the dissipation theory. Moreover, this study summarizes the entropy production rate change characteristics of diverse wear processes and proposes a method to predict wear failure in advance based on these features. The proposed wear failure prediction model is demonstrated through application examples. This model provides a theoretical and computational foundation for preventing wear failure of critical components and supports wear failure prediction of subsequent friction pair digital twin systems.