Twisted pair cable fault diagnosis via random forest machine learning

• Main Authors: Ghazali, N. B., Seman, F. C., Isa, K., Ramli, K. N., Z. Abidin, Z., Mustam, S. M., Haek, Haek, Z. Abidin, A. N., Asrokin, A.
• Format: Article
• Language: English
• Published: Tech Science Press 2022
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.uthm.edu.my/6899/1/J14092_e1740ec39d908019e6fd019d22b17343.pdf; http://eprints.uthm.edu.my/6899/; https://doi.org/10.32604/cmc.2022.023211

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 System. The network performance depends on the occurrence of cable fault along the copper cable. Currently...