  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.
