Comparative Analysis of Support Vector Machine, Random Forest and k-Nearest Neighbor Classifiers for Predicting Remaining Usage Life of Roller Bearings

This research article aims to predict the remaining usage time of roller bearings using machine learning algorithms. The specific classifiers employed in this study are Support Vector Machines, Random Forest Classifier, and k-Nearest Neighbors. The predictive model takes into account various features including temperature, speed, load, dimensions of the inner and outer rings, width, vibration amplitude, vibration frequency, lubricant type, and lubricant viscosity. Data for training and testing the model were collected using a custom-made single bearing test rig. The target output variables are divided into intervals representing different percentages of remaining usage time. Principal component analysis (PCA) is utilized to identify the most influential features from the data. A ten-fold cross-validation method is employed for training and testing the classifiers. The features extracted through PCA are then fed into the classification model. The results show that the Support Vector Machines achieve the highest mean classification accuracy of 96.74%, followed by the Random Forest Classifier with 95.95%, and the k-Nearest Neighbors classifier with 91.77%. The study concludes that the Support Vector Machines outperform the Random Forest Classifier and k-Nearest Neighbors. Future research directions include exploring the application of deep learning algorithms to further enhance the predictive accuracy of the model. Additionally, conducting experiments with a larger and more diverse dataset, encompassing various operating conditions and types of bearings, would provide a broader understanding of the model's performance and generalizability.