论文标题
在噪声OTDR信号的情况下，光纤连接中的反射事件检测和表征的卷积神经网络
Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals
论文作者
论文摘要
快速准确的故障检测和光纤电缆中的定位对于确保光网的生存能力和可靠性非常重要。因此，对于实时光纤故障检测和诊断利用了通过光学时域反射仪（OTDR）仪器获得的遥测数据，为实时光纤故障检测和诊断提供了至关重要的需求。在本文中，我们提出了一种基于卷积神经网络（CNN）的新型数据驱动方法，以检测和表征嘈杂的模拟OTDR数据的纤维反射故障，其SNR（信噪比）值在0 dB到30 dB变化，并结合了反射事件模式。在我们的模拟中，与常规使用的技术相比，我们达到了较高的错误警报率和更高的定位精度，并且具有更高的定位精度。
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.
