Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory

TL;DR

This paper introduces a multi-task LSTM-based model for detecting, locating, and characterizing fiber faults using OTDR data, significantly improving accuracy and speed over traditional methods.

Contribution

The paper presents a novel multi-task learning approach leveraging LSTM to enhance fiber fault detection and characterization from OTDR data.

Findings

High detection accuracy even at low SNR

Precise fault localization within short measurement times

Outperforms conventional fault diagnosis techniques

Abstract

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.