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(a) Strain distribution along underground telecom cable parallel to a road, crossing a road at z ≈ 320 m. (b) Detail of (a): surface wave propagation indicating road damage (fs = 1 kHz, fp = 50 kHz, 2 m spatial resolution, 20.4 cm sampling resolution, strain average of 3 samples along z and t).
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We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The appl...
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... cable runs in parallel to a road and crosses a two-lane road at z ≈ 320 m. Various quasi-static and dynamic events in the nε-range can be seen in Figure 3: a vehicle stopping at 350 m, numerous cars crossing the cable at 320 m, and surface wave propagation due to the vehicle passing a roadway damage at 375 m. These results demonstrate that highresolution strain measurement along telecom cables can provide a wide range of useful information. ...
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... Further, multiplying the round-trip time of the pulse with the speed of light gives us the location of the perturbation. DAS sensors are used across a wide range of fields, including but not limited to Structural Health Monitoring, 3 Transportation Tracking, 4 Pipeline Surveillance, 5, 6 Telecommunication Line Monitoring, and Perimeter Security. In order to lower the False Alarm Rate (FAR) and intervene appropriately, these applications require accurate detection and classification of the acoustic source. ...
... Applications for such a device are numerous and diverse, spanning a wide range of fields. One of these applications can be structural health monitoring, 3 where we can detect cracks in high-rise buildings or bridges. Another application could be perimeter security to detect intrusions or fence cutting or climbing. ...
... A detector is then used to measure the reflected light (Rayleigh signal) from the fibre as pulses of light travel along the fibre. DAS system has been adopted in various monitoring applications such as pipeline condition monitoring, intrusion detection, seismic monitoring, and railway condition monitoring [10,11,12,13,14,15]. ...
... Distributed acoustic sensing (DAS) is a powerful fiber optic technique that can detect vibrations with a resolution of a few meters along a standard telecom glass fiber many tens of kilometers long. With these unique and still improving capabilities, DAS is increasingly used in applications such as intrusion detection along a perimeter, leak monitoring along pipelines, monitoring of sub-sea cables, or seismic monitoring [1][2][3]. In these examples, the long-range sensing capabilities with a single fiber are used. ...
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars. The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods. The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h. The analysis methods presented here will help to establish routines for near real-time train tracking and train integrity analysis.
Distributed Acoustic Sensors (DAS) are able to monitor in real-time acoustic excitation and mechanical vibration of the external environment for many kilometers along the length of the optical fiber. In the oil industry, the technology allows the monitoring of well structural health, identifying flow profile, acquiring seismic data, updating reservoir information and feeding geological models. The spatial resolution of DAS is a limiting factor on the quality of information gathered. In this paper, we propose and analyze a novel method of measuring local dynamic strain of structures using DAS. The method uses serpentine configurations to measure the frequency spectrum of the in-phase strain signals at discrete regions of the structure, enabling DAS to determine, among other vibration parameters, the strain frequency response function (SFRF). An experimental study of the sensor application with different arrangements in a small structure has been performed. The structure in the experiment consists of a free-free aluminum beam of approximately 2.7 meters. Experiment results show agreement with numerical results developed by the Finite Element Method (FEM) and using reference sensors, demonstrating a significant improvement of the DAS SFRF accuracy using the serpentine configuration and up to 12.9 dB increase in Peak-to-Noise Ratio (PNR) for low excitation forces. The recovered response enabled the reconstruction of mode shapes.
This work aims at the detection and classification of Distributed Acoustic Sensor (DAS) acquired acoustic signals. We obtained the data by probing an optical fiber with light pulses and gauging the Rayleigh backscatter. Said data contains four different classes; Walking, Shovel and Pick digging as well as Hammer hitting. We first proceed by detecting the event and its location along the fiber and extracting it from the random noise using Spiked Random Matrix Theory (RMT) models, namely Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions. We then label the datasets accordingly and proceed with the classification process using machine learning algorithms. For this, we test and evaluate Convolutional Neural Networks (CNN), which has been proven to provide high accuracies in similar studies, taking the spectrograms of the signals as our network's input. We conclude by providing the performance of our CNN architecture and propose a few options to further improve the performance of the model.
