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

作     者：Palaniappan, Rajkumar 

作者机构：College of Engineering Department of Mechatronics Engineering University of Technology Bahrain Salmabad Bahrain 

出 版 物：《Informatica (Slovenia)》 (Informatica)

年 卷 期：2024年第48卷第7期

页      面：39-52页

核心收录：

学科分类：0710[理学-生物学] 0828[工学-农业工程] 08[工学] 0901[农学-作物学] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 0836[工学-生物工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Support vector machines 

摘      要：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. © 2024 Slovene Society Informatika. All rights reserved.