| Summary: | 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.
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