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

Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals