Prediction of contact resistance of electrical contact wear using different machine learning algorithms

作     者：Zhen-bing CAI Chun-lin LI Lei YOU Xu-dong CHEN Li-ping HE Zhong-qing CAO Zhi-nan ZHANG Zhen-bing CAI;Chun-lin LI;Lei YOU;Xu-dong CHEN;Li-ping HE;Zhong-qing CAO;Zhi-nan ZHANG

作者机构：Tribology Research InstituteSouthwest Jiaotong UniversityChengdu 610031China State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghai 200240China 

出 版 物：《Friction》 (摩擦（英文版）)

年 卷 期：2024年第12卷第6期

页      面：1250-1271页

核心收录：

学科分类：12[管理学] 080503[工学-材料加工工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Sichuan Science and Technology Planning Project(2022ZYD0029 and 2022JDJQ0019) the National Natural Science Foundation of China(51875343) 

主　　题：electrical contact wear contact resistance machine learning(ML) neural network 

摘      要：H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all *** the wear behavior of electrical contact pairs is particularly complex when they are in service,we evaluated the effects of load,sliding velocity,displacement amplitude,current intensity,and surface roughness on the changes in contact *** learning(ML)algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact *** forest(RF),support vector regression(SVR)and BP neural network(BPNN)algorithms were used to establish RF,SVR and BPNN models,respectively,and the experimental data were trained and *** was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after *** analysis shows that the load and current have great influence on the predicted electrical contact *** wear behavior of electrical contacts is influenced by factors such as load,sliding speed,displacement amplitude,current intensity,and surface roughness during *** learning algorithms can predict the electrical contact performance after wear caused by these *** results indicate that an increase in load,current,and surface roughness leads to a decrease in stable mean resistance,while an increase in displacement amplitude and frequency results in an increase in stable mean resistance,leading to a decline in electrical contact *** reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors,three algorithms(random forest(RF),support vector regression(SVR),and BP neural network(BPNN))were used to train and test experimental results,resulting in a machine learning model suitable for predicting the stable mean