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

• URL: http://arxiv.org/abs/2203.12604v1
• Date: Sat, 19 Mar 2022 08:37:40 GMT
• Title: Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
• Authors: khouloud Abdelli, Helmut Griesser, Carsten Tropschug, and Stephan Pachnicke
• Abstract summary: The proposed method combines a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from -5 dB to 15 dB. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network [15.913517836391357]
  Real-world radar signals are often corrupted by a blend of artifacts, including but not limited to unwanted echo, sensor noise, intentional jamming, and interference. This study introduces Blind Radar Signal Restoration using an Operational Generative Adversarial Network (BRSR-OpGAN) This approach is designed to improve the quality of radar signals, regardless of the diversity and intensity of the corruption.
  arXiv  Detail & Related papers  (2024-07-18T23:55:48Z)
• M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [63.39134873744748]
  Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. This paper proposes a novel noise-resistant M3DM-NR framework to leverage strong multi-modal discriminative capabilities of CLIP. Extensive experiments show that M3DM-NR outperforms state-of-the-art methods in 3D-RGB multi-modal noisy anomaly detection.
  arXiv  Detail & Related papers  (2024-06-04T12:33:02Z)
• Quantifying Noise of Dynamic Vision Sensor [49.665407116447454]
  Dynamic visual sensors (DVS) are characterised by a large amount of background activity (BA) noise. It is difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. A new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA)
  arXiv  Detail & Related papers  (2024-04-02T13:43:08Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems [11.190815358585137]
  Joint channel estimation and signal detection is crucial in frequency division multiplexing systems. Traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain.
  arXiv  Detail & Related papers  (2023-03-07T06:34:04Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals [0.0]
  We propose a novel data driven approach based on convolutional neural networks (CNNs) to detect and characterize the fiber reflective faults. In our simulations, we achieved a higher detection capability with low false alarm rate and greater localization accuracy even for low SNR values.
  arXiv  Detail & Related papers  (2022-03-19T09:20:40Z)
• Heart Sound Classification Considering Additive Noise and Convolutional Distortion [2.63046959939306]
  Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both types of distortions are present in the cardiac auscultation sound. The proposed method paves the way towards developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.
  arXiv  Detail & Related papers  (2021-06-03T14:09:04Z)
• Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method [0.0]
  We present a novel method for inferring ground-truth signal from degraded signals. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model.
  arXiv  Detail & Related papers  (2020-09-07T13:24:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.