Abstract
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.
Highlights
Worldwide fixed access technologies depend on fiber-optic and copper-wires technologies to deliver high-speed internet access to the end customers
For on-site installation and repair, the EXFO test gear is commonly used by the telco engineer to observe the Digital Subscriber Line (DSL) network performance, here, the same test gear was used to verify the cable fault emulations conducted in the laboratory
Multi-Service Access Network (MSAN) data were exported into WEKA software and the outcome showed that random forests algorithms (RFs) algorithm provided the highest accuracy which is about 80%
Summary
Worldwide fixed access technologies depend on fiber-optic and copper-wires technologies to deliver high-speed internet access to the end customers. TDR is a wellknown method to measure the characteristics of the electrical lines, impedance of discontinuities as a function of time or distance, reflection signal can be translated to determine the faults caused by a splice, cable transition, and mismatched cable connections [1] This method is reliable to detect the fault type and localization; test gears are needed with manual intervention to the field site. The VDSL2 network cards and Plain Old Telephone Service Line (POTS) cards are integrated at MSAN, and this is the important key factor that allows both telephone and internet services to be offered using the same copper cables infrastructure [15] This network topology is usually established at the suburban site where the capital expenditure of the telco needs to reflect the customer’s populations in that particular area. These raw data are in text file (.txt format) and later processed into comma separated values file (.csv format) for data preparation
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