  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.
