Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

作     者：N.B.Ghazali F.C.Seman K.Isa K.N.Ramli Z.Z.Abidin S.M.Mustam M.A.Haek A.N.Z.Abidin A.Asrokin 

作者机构：Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaBatu PahatJohor TM Research&DevelopmentCyberjayaMalaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第6期

页      面：5427-5440页

核心收录：

学科分类：0810[工学-信息与通信工程] 0711[理学-系统科学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors received the funding from Smart Challenge Fund(SR0218I100) GPPS Grant VOT H404,from Ministry of Science,Technology and Innovation Malaysia,and Research Management Centre(RMC)of Universiti Tun Hussein Onn Malaysia(UTHM) 

主　　题：Twisted pairs random forest machine learning cable fault DSL 

摘      要：Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line(DSL)Access Network *** network performance depends on the occurrence of cable fault along the copper ***,most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site,which may be resolved using data analytics and machine learning *** paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning *** DSL Access Network is emulated in the laboratory to accommodate VDSL2 Technology with various types of cable fault along the cable distance between 100 m to 1200 ***,the line operation parameters and loop line testing parameters are collected and used to ***,the feature transformation,a knowledge-based method,is utilized to pre-process the fault ***,the random forests algorithms(RFs),a data-driven method,are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault ***,the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network *** results show that the cable fault detection has an accuracy of more than 97%,with less minimum absolute error in cable fault localization of less than 11%.The proposed algorithm may assist the telecommunication service provider to initiate automated cable faults identification and troubleshooting in the DSL Access Network System.