  Fast and accurate fault detection and localization in fiber optic cables is
extremely important to ensure the optical network survivability and
reliability. Hence there exists a crucial need to develop an automatic and
reliable algorithm for real time optical fiber fault detection and diagnosis
leveraging the telemetry data obtained by an optical time domain reflectometry
(OTDR) instrument. In this paper, we propose a novel data driven approach based
on convolutional neural networks (CNNs) to detect and characterize the fiber
reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise
ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns.
In our simulations, we achieved a higher detection capability with low false
alarm rate and greater localization accuracy even for low SNR values compared
to conventionally employed techniques.
