  Passive optical network (PON) systems are vulnerable to a variety of
failures, including fiber cuts and optical network unit (ONU)
transmitter/receiver failures. Any service interruption caused by a fiber cut
can result in huge financial losses for service providers or operators.
Identifying the faulty ONU becomes difficult in the case of nearly equidistant
branch terminations because the reflections from the branches overlap, making
it difficult to distinguish the faulty branch given the global backscattering
signal. With increasing network size, the complexity of fault monitoring in PON
systems increases, resulting in less reliable monitoring. To address these
challenges, we propose in this paper various machine learning (ML) approaches
for fault monitoring in PON systems, and we validate them using experimental
optical time domain reflectometry (OTDR) data.
