故障排除
计算机科学
生存能力
反射计
光时域反射计
实时计算
断层(地质)
故障检测与隔离
光纤
任务(项目管理)
软件部署
可靠性工程
电子工程
嵌入式系统
时域
光纤传感器
计算机网络
人工智能
工程类
电信
地震学
执行机构
地质学
渐变折射率纤维
操作系统
系统工程
计算机视觉
作者
Khouloud Abdelli,Helmut Grießer,Peter Ehrle,Carsten Tropschug,Stephan Pachnicke
出处
期刊:Journal of Optical Communications and Networking
[The Optical Society]
日期:2021-05-10
卷期号:13 (10): E32-E32
被引量:25
摘要
To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and with high accuracy. With the rapid advancement of telemetry technology and data analysis techniques, data-driven approaches leveraging telemetry data to tackle the fault diagnosis problem have been gaining popularity due to their quick implementation and deployment. In this paper, we propose a novel multi-task learning model based on long short-term memory (LSTM) to detect, locate, and estimate the reflectance of fiber reflective faults (events) including the connectors and the mechanical splices by extracting insights from monitored data obtained by the optical time domain reflectometry (OTDR) principle commonly used for troubleshooting of fiber optic cables or links. The experimental results prove that the proposed method: (i) achieves a good detection capability and high localization accuracy within short measurement time even for low SNR values; and (ii) outperforms conventionally employed techniques.
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