Filtern
Dokumenttyp
Sprache
- Englisch (10)
- Mehrsprachig (1)
Schlagworte
- Distributed acoustic sensing (11) (entfernen)
Organisationseinheit der BAM
Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring
(2022)
Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks).
A novel distributed acoustic sensing technique is proposed that exploits both phase and amplitude of the Rayleigh backscattered light to quantify the environmental variation. The system employs a wavelength-scanning laser and an imbalanced Mach-Zehnder interferometer to acquire the reflection spectra and the phase of the detected light, respectively. Fading-free and low-frequency measurements are realized via the crosscorrelation of the reflection spectra. The discrete crosscorrelation is used to circumvent the nonlinear frequency sweeping of the laser. Based on the phase of the backscattered light, it is possible to quantify fast environmental variations. The whole system requires no hardware modification of the existing system and its functionality is experimentally validated. The proposed system has the potential to monitor ground motion/movement at very low frequency band like subsidence around mining areas and at high frequency band like earthquakes and vibrations induced by avalanches.
We report on the successful application of distributed acoustic sensing (DAS) to the detection of partial discharge (PD). A detection limit of about 1 nC discharge magnitude was achieved for PD in a real-scale model of a high voltage termination. Dedicated ultrasonic fibre-optic transducers were interrogated using coherent optical time-domain Rayleigh backscatter reflectometry (C-OTDR). Random quadrature demodulation was employed for retrieving relevant acoustic information from the raw C-OTDR backscatter traces. To our knowledge, our results are a first-time demonstration that quasi-distributed fibre-optic acoustic sensing is a candidate technology for the acoustic partial discharge monitoring of power cable joints and terminations.
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.
A distributed acoustic sensor (DAS) based on wavelength-scanning coherent optical time domain reflectometer (WS-COTDR) is tested in an underground gas storage based on a multimode fiber for the first time to the best of our knowledge. The WS-COTDR acquires the reflection spectrum by scanning the wavelength of the laser and uses the spectra to calibrate strain. It overcomes the fading problem which is common for all DAS systems, and possesses the advantage of simple configuration and low-cost. The working principle of the system is well explained and the measurement results are presented. Various artificial and natural acoustic events have been successfully detected by the WS-COTDR. Such a sensor demonstrates several advantages over the traditional DAS systems, so it is supposed to be widely used in the oil and gas industry.
A phase-sensitive optical time domain reflectometer based on coherent heterodyne detection of geometric phase in the beat signal of light, is reported for the first time to our knowledge. The use of the geometric phase to extract strain makes it immune to polarisation diversity fading. This is because a polarisation mismatch between the interfering beams is not a hindrance to its measurement. The geometric phase is calculated using the amplitude of the beat signal and individual beam intensities without any need for phase unwrapping. It is measured per beat period and can be equated with the traditionally measured dynamic phase with appropriate scaling. The results show that the system based on the geometric phase successfully measures strain, free from polarisation mismatch fading and phase unwrapping errors, providing a completely novel solution to these problems.
Online condition monitoring of submarine power cables helps to avert failures and damages produced by mechanical impacts. We report, to our knowledge for the first time, on investigations regarding the feasibility of distributed fiber optic acoustic sensors based on C-OTDR, with the sensor fibres being embedded in the cable, to detect vibrations due to mechanical disturbances along the cable. We present first results of sensing experiments where acoustic signals are transmitted through water to simulate the corresponding submarine conditions. Furthermore, we show results evaluating the usefulness of fibre commonly embedded in existing power cable designs for our sensing purposes.