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- Distributed acoustic sensing (5)
- Fiber optics sensors (3)
- Optical time domain reflectometry (3)
- Structural health monitoring (3)
- Artificial neural networks (2)
- DAS (2)
- Distributed fiber optic sensing (2)
- Distributed strain sensing (2)
- Electronic speckle pattern interferometry (2)
- Optical fiber sensor (2)
Organisationseinheit der BAM
Der Beitrag konzentriert sich auf bildgebende Messverfahren, die der dreidimensionalen Bestimmung mechanischer, geometrischer und kinematischer Parameter von Materialien und Bauteilen dienen. Das sind hier insbesondere die Mehrbildfotogrammetrie sowie die Stereomessverfahren, die auf folgenden physikalisch-optischen Grundlagen beruhen: die zeit- und raumveränderliche Projektion von Lichtstreifensystemen, die Bildkorrelation mittels Grauwertmustern und die Verfolgung kontrastreich applizierter Punkttargets. Letztendlich werden aus den gemessenen Punktkoordinaten unterschiedlicher Dichte und Belastungszustände die gewünschten 3D-Parameter abgeleitet. Hierzu gehören z. B. die Oberflächengestalt und deren Verformung, approximierte 3D-Geometrielemente, veränderliche 6DoF-Starrkörperorientierungen, Vektorverschiebungsfelder, Oberflächendehnung, Koordinaten-Zeit-Funktionen, Geschwindigkeit und Beschleunigung. Diese Kenngrößen sind beispielsweise für die Ermittlung mechanischer Werkstoffeigenschaften und -kennwerte bis hin zur Form- und Verformungsmessung von Bauteilen in gezielt quasi-statisch oder dynamisch beanspruchenden Prüfeinrichtungen wichtig. Vorteilhaft für die praktische Anwendung dieser Verfahren ist insbesondere, dass sie berührungslos arbeiten, kombiniert werden können und skalierbar sind z. B. hinsichtlich Messvolumen, Auflösung, Genauigkeit und Koordinatensystemen der zu bestimmenden 3D-Punktkoordinaten an Materialoberflächen bzw. Komponenten.
Ausgehend von der Beschreibung der messtechnischen Prinzipien dieser Verfahren sollen zwei Anwendungsbeispiele aus dem Bereich der Gefahrgutbehäl-terprüfung im Rahmen von Bauartzulassungen und der normungsbegleitenden Mate-rialforschung den Stand in der Anwendungspraxis aufzeigen und einen Eindruck hinsichtlich der flexiblen Einsatzmöglichkeiten dieser optischen 3D-Verfahren verdeutlichen.
Strain sensors embedded in or attached to structural components have to measure the real deformation of the structure over the whole period of use. The user must know how reliably installed sensors provide strain measurement results. For this purpose, test facilities or coupon tests are used. In order to characterize the strain transfer quality from the host structure into surface-applied strain sensors, a unique testing facility has been developed. This facility can be used both for fiber optic and resistance strain sensors. Originally developed for fiber Bragg grating based sensors, the KALFOS facility (=calibration of fiber optic sensors) uses Digital Image Correlation (DIC) and Electronic Speckle Pattern Interferometer (ESPI) as unbiased referencing methods. It is possible to determine experimentally the strain transfer mechanism under combined thermal and mechanical loading conditions. This experimental characterization method will reveal weaknesses in commonly used strain sensors, and the investigation of the material systems used for fiber optic and other strain sensors (particularly the coating/substrate - adhesive combination). The KALFOS facility allows matching of specific measurement requirements with environmental conditions.
The feasibility of traffic monitoring along a major urban road using Distributed Acoustic Sensing (DAS) is demonstrated. We present measurement results of conventional intensity-based DAS along a dark fiber in a tubed fiber optic cable buried roadside. With only minimal postprocessing different classes of traffic participants can be distinguished (cars, bikes, pedestrians). Different methods for pre-processing raw data are compared, especially with regard to providing suitable inputs for pattern recognition algorithms. Furthermore, the various challenges for automatized vehicle detection and classification related to varying sensitivity and inhomogeneous signal propagation are discussed. Sensitivity fluctuations and variations are in part inherent to the measurement technology and originate in part in ground conditions. The measurement quality and usefulness for traffic monitoring of intensity-based DAS is compared to that of state-of-the-art phase-resolved DAS, allowing quantitative evaluation of vibration signals.
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.
We propose the use of alternating pulse wavelengths in a direct-detection coherent optical time domain reflectometry (C-OTDR) setup not only to measure strain and temperature changes but also to determine the correct algebraic sign of the change. The sign information is essential for the intended use in distributed mode shape analysis of civil engineering structures. Correlating relative backscatter signal shifts in the temporal/signal domain allows for measuring with correct magnitude and sign. This novel approach is simulated, experimentally implemented and demonstrated for temperature change measurement at a spatial resolution of 1 m.
A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification.
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.