Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (15)
- Vortrag (13)
- Zeitschriftenartikel (12)
- Posterpräsentation (6)
Sprache
- Englisch (33)
- Deutsch (12)
- Mehrsprachig (1)
Schlagworte
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (46) (entfernen)
Eingeladener Vortrag
- nein (13)
Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system.
Im vorliegenden Bericht wurde untersucht, Spanndrahtbrüche in einem Brückenträger sowohl mittels Schallemissionsanalyse (SEA) als auch mit (eingebetteter) verteilter faseroptischer akustischer Sensorik (DAS) zu erkennen. Die Ergebnisse zeigen, dass die Bruchsignale anhand beider Messverfahren detektiert werden können. Die jeweils detektierten Drahtbrüche werden miteinander verglichen. Durch eine Gegenüberstellung werden SEA und DAS detailliert dargestellt.
Geometric phase measured per beat period in a ϕ-OTDR based on coherent heterodyne detection is used to measure strain. Proposed method is robust to polarisation mismatch fading as a polarisation mismatch between interfering beams is not a hindrance to the measurement of the geometric phase. The Geometric phase is a function of the intensities of the interfering beams as well as the envelope of the beat signal. Its calculation does not require phase unwrapping and accordingly does not suffer the phase unwrapping errors. It is required to be equated with the traditionally measured phase by applying a scaling factor. The spatial resolution of the measured strain is reduced as it is calculated per beat period. Results are verified using a piezo-electric transducer inline a fiber-under-test.
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.
After describing the basics of distributed fiber optic sensing in general and distributed fiber optic acoustic sensing (DAS) in particular, several currently and previously investigated fields of application of DASin infrastructure monitoring and seismic monitoring are presented. A focus is placed on seismic measurements and DAS measurements determining the dynamic responses of concrete structures to certain stimuli.
We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µɛ to 1380 µɛ, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µɛ, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor’s performance is provided.
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.