Quantitative prediction of wear failure based on dissipative methodology under dry friction

Abstract

The evaluation and prediction on failure of friction pair are important for improving equipment reliability and life span. In engineering practice, researchers often determine the severe-wear stage by monitoring changes in wear and the friction coefficient. However, existing criteria for evaluating the wear process are nebulous, leading to the ambiguous relationship between the measured parameters and the wear process which cannot be precisely quantified. In this study, based on dissipation theory, an exploration into the system entropy production rate variations throughout the wear process has been conducted. This investigation reveals that entropy production rate variations at different wear stages, providing a basis for segmenting the wear process. Through experimental methods, a clear transitional stage with substantial entropy production rate variations between the mild-wear and severe-wear stages has been discovered. These findings gave a novel method for predicting wear failure. The efficacy of this proposed approach has been validated by experiment, thus laying a theoretical groundwork for early failure prediction of critical components principally prone to wear failure. Furthermore, this work provides the foundation for the construction of visual data system in friction-wear digital twin systems.