Twisted pair cable fault diagnosis via random forest machine learning
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tech Science Press
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6899/1/J14092_e1740ec39d908019e6fd019d22b17343.pdf |
| _version_ | 1796869453998194688 |
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| author | 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. |
| author_facet | 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. |
| author_sort | Ghazali, N. B. |
| collection | UTHM |
| description | 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,
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 algorithm. This paper presents a fault diagnosis method for twisted
pair cable fault detection based on knowledge-based and data-driven machine
learning methods. The 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 m. Firstly, the line operation
parameters and loop line testing parameters are collected and used to analyze.
Secondly, the feature transformation, a knowledge-based method, is utilized
to pre-process the fault data. Then, the random forests algorithms (RFs), a
data-driven method, are adopted to train the fault diagnosis classifier and
regression algorithm with the processed fault data. Finally, the proposed
fault diagnosis method is used to detect and locate the cable fault in the
DSL Access Network System. The 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. |
| first_indexed | 2024-03-05T21:55:08Z |
| format | Article |
| id | uthm.eprints-6899 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:55:08Z |
| publishDate | 2022 |
| publisher | Tech Science Press |
| record_format | dspace |
| spelling | uthm.eprints-68992022-04-12T06:37:40Z http://eprints.uthm.edu.my/6899/ Twisted pair cable fault diagnosis via random forest machine learning 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. TJ Mechanical engineering and machinery 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, 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 algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods. The 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 m. Firstly, the line operation parameters and loop line testing parameters are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to pre-process the fault data. Then, the random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault data. Finally, the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network System. The 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. Tech Science Press 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/6899/1/J14092_e1740ec39d908019e6fd019d22b17343.pdf Ghazali, N. B. and Seman, F. C. and Isa, K. and Ramli, K. N. and Z. Abidin, Z. and Mustam, S. M. and Haek, Haek and Z. Abidin, A. N. and Asrokin, A. (2022) Twisted pair cable fault diagnosis via random forest machine learning. Computers, Materials & Continua, 71 (3). pp. 5427-5440. https://doi.org/10.32604/cmc.2022.023211 |
| spellingShingle | TJ Mechanical engineering and machinery 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. Twisted pair cable fault diagnosis via random forest machine learning |
| title | Twisted pair cable fault diagnosis via random forest machine learning |
| title_full | Twisted pair cable fault diagnosis via random forest machine learning |
| title_fullStr | Twisted pair cable fault diagnosis via random forest machine learning |
| title_full_unstemmed | Twisted pair cable fault diagnosis via random forest machine learning |
| title_short | Twisted pair cable fault diagnosis via random forest machine learning |
| title_sort | twisted pair cable fault diagnosis via random forest machine learning |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/6899/1/J14092_e1740ec39d908019e6fd019d22b17343.pdf |
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