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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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
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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.
AbstractList	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.
Author	Tropschug, Carsten Pachnicke, Stephan Grieser, Helmut Abdelli, Khouloud
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SubjectTerms	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
Title	Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
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