论文标题
基于去索的卷积自动编码器和双向长短期内存的嘈杂的OTDR痕迹中的光纤故障检测和定位
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
论文作者
论文摘要
光学时域反射仪（OTDR）已广泛用于表征光纤连接并检测和定位纤维故障。 OTDR痕迹容易被不同种类的噪声扭曲，从而导致反向散射信号的模糊，从而导致误导性的解释和更繁琐的事件检测任务。为了解决这个问题，提出了一种新型方法，该方法结合了降级卷积自动编码器（DCAE）和双向长短期记忆（BILSTM），提出了前者的噪声删除OTDR信号，后者用于噪声，以用于检测到故障，定位和诊断为输入。所提出的方法应用于不同水平的输入SNR的嘈杂OTDR信号，范围为-5 dB至15 dB。实验结果表明：（i）DCAE有效地降低了OTDR痕迹，并且它优于其他深度学习技术和常规的剥离方法； （ii）与使用嘈杂的OTDR信号训练的同一模型相比，BilstM的高检测和诊断精度为96.7％，提高了13.74％。
Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distorted by different kinds of noise, causing blurring of the backscattered signals, and thereby leading to a misleading interpretation and a more cumbersome event detection task. To address this problem, a novel method combining a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the former is used for noise removal of OTDR signals and the latter for fault detection, localization, and diagnosis with the denoised signal as input. The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i) the DCAE is efficient in denoising the OTDR traces and it outperforms other deep learning techniques and the conventional denoising methods; and (ii) the BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an improvement of 13.74% compared to the performance of the same model trained with noisy OTDR signals.
