Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory

TL;DR

This paper introduces a combined deep learning approach using a denoising autoencoder and BiLSTM to improve fault detection and localization in noisy OTDR fiber optic signals, significantly enhancing accuracy.

Contribution

It presents a novel method that effectively denoises OTDR signals and accurately detects faults, outperforming existing techniques in noisy environments.

Findings

DCAE effectively denoises OTDR traces, surpassing other methods.

BiLSTM achieves 96.7% fault detection accuracy, 13.74% higher than noisy input models.

Method works across various SNR levels from -5 dB to 15 dB.

Abstract

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.