The Prediction of Wear Depth Based on Machine Learning Algorithms

Abstract

In this work, ball-on-disk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness. In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, namely Random Forest (RF), K-neighborhood (KNN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were applied to predict wear depth. By analyzing the performance of several ML algorithms, it is demonstrated that ball bearing wear depth can be estimated by ML models by inputting different parameter variables. A comparative analysis of the performance of the different models revealed that XGB was more accurate than the other ML models at anticipating wear depth. Further analysis of the attribute of feature importance and correlation heatmap of the Pearson correlation reveals that each input feature has an effect on wear.