Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal (OLT), the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. However, faulty branch isolation becomes very challenging when the reflections originating from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML) based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade, if the PON system (adopted for the generation of the training data) has changed, e.g. by adding more branches or varying the length difference between two neighboring branches. etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from PON system.

翻译：无源光网络（PON）已成为一种极具前景的宽带接入网络解决方案。为确保可靠传输并满足服务等级协议，PON系统需要持续监控，以便快速识别并定位网络故障。通常，PON系统中的服务中断主要源于光纤断裂和光网络单元（ONU）的发射/接收器故障。当ONU与光线路终端（OLT）距离不同时，可通过分析记录的光时域反射仪（OTDR）迹线来识别故障ONU或支路。然而，当两个或多个长度相近的支路产生的反射信号重叠时，故障支路隔离变得极具挑战性，使得从全局后向散射信号中判别故障支路非常困难。近年来，基于机器学习（ML）的方法在管理PON系统光故障方面展现出巨大潜力。这类方法在基于同一PON系统生成的数据上进行训练和测试时表现良好，但若PON系统（用于生成训练数据）发生变化（例如增加支路数量或改变相邻支路的长度差等），其性能可能严重下降。每次网络变化都需要对ML模型重新训练，过程耗时。本文针对上述问题，提出一种通用ML方法——该方法独立于网络架构进行训练，用于在支路长度相近的情况下，基于OTDR信号识别PON系统中的故障支路。该方法可应用于任意PON系统，无需因网络变化而重新训练。实验结果基于PON系统数据验证了所提方法的有效性。