Transmission cable fault detector based on waveform reconstruction

In order to improve the accuracy of cable fault position location at a low cost and make the testing results intuitive, a cable fault detector based on wave form reconstruction is designed. In this detector, the cable fault position is located based on the time-domain pulse reflection （TDR） principle. A pulse waveform is injected in the tested cable, and a high-speed comparator with changeable reference voltages is used to binarize the test pulse waveform to a binary sequence on a certain voltage. Through scanning the reference voltage in a full voltage range, multi-sequences are acquired to reconstruct the pulse waveform transmission in the cable, and then the pulse attenuation feature, electrical open circuit fault, electrical short circuit fault, and the fault position of the cable are diagnosed. Experimental results show that the designed cable fault detector can determine the fault type and its position of the cable being tested, and the testing results are intuitive.

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,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.

Distributed Intrusion Monitoring System With Fiber Link Backup and On-Line Fault Diagnosis Functions

A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line fault diagnosis functions was proposed. A 1 ~ N optical switch was intelligently controlled by a peripheral interface controller （PIC） to expand the fiber link from one channel to several ones to lower the cost of the long or ultra-long distance intrusion monitoring system and also to strengthen the intelligent monitoring link backup function. At the same time, a sliding window auto-correlation method was presented to identify and locate the broken or fault point of the cable. The experimental results showed that the proposed multi-channel system performed well especially whenever any a broken cable was detected. It could locate the broken or fault point by itself accurately and switch to its backup sensing link immediately to ensure the security system to operate stably without a minute idling. And it was successfully applied in a field test for security monitoring of the 220-km-length national borderline in China.