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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.
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.
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.
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).
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.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.
In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single ug were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring Approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.
Distributed vibration sensing in optical fibers opened entirely new opportunities and penetrated various sectors from security to seismic monitoring. Here, we demonstrate a most simple and robust approach for dynamic strain measurement using wavelength-scanning coherent optical time domain reflectometry (C-OTDR). Our method is based on laser current modulation and Rayleigh backscatter shift correlation. As opposed to common single-wavelength phase demodulation techniques, also the algebraic sign of the strain change is retrieved. This is crucial for the intended applications in structural health monitoring and modal analysis. A linear strain response down to 47.5 pε and strain noise of 100 pε/√Hz is demonstrated for repetition rates in the kHz range. A field application of a vibrating bridge is presented. Our approach provides a cost-effective high-resolution method for structural vibration analysis and geophysical applications.