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

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 inte...

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Bibliographic Details
Published in:Journal of lightwave technology Vol. 40; no. 8; pp. 2254 - 2264
Main Authors: Abdelli, Khouloud, Grieser, Helmut, Tropschug, Carsten, Pachnicke, Stephan
Format: Journal Article
Language:English
Published: New York IEEE 15.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Backscattering
Bidirectional long short-term memory
Blurring
Computer architecture
Convolution
convolutional autoencoder
Decoding
denoising
Fault detection
Fault location
Fiber optics
intelligent fault detection and diagnosis
Localization
Logic gates
Microprocessors
Noise reduction
Optical fibers
Optical memory (data storage)
OTDR (optical time domain reflectometry)
Short term
ISSN:0733-8724, 1558-2213
Online Access:Get full text
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Summary: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.
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ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2021.3138268