Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-474832
Real-time dynamic strain sensing in optical fibers using artificial neural networks
- 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.,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.…
Autor*innen: | Sascha LiehrORCiD, L. A. Jäger, Christos Karapanagiotis, Sven Münzenberger, Stefan KowarikORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Optics Express |
Jahr der Erstveröffentlichung: | 2019 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | Optical Society of America |
Jahrgang/Band: | 27 |
Ausgabe/Heft: | 5 |
Erste Seite: | 7405 |
Letzte Seite: | 7425 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau | |
Freie Schlagwörter: | Artificial neural network; Coherent optical time domain reflectometry; Distributed acoustic sensing; Distributed strain sensing; Distributed vibration sensing; Optical fiber sensor; Real time measurement |
Themenfelder/Aktivitätsfelder der BAM: | Energie |
Infrastruktur | |
Infrastruktur / Security | |
DOI: | 10.1364/OE.27.007405 |
URN: | urn:nbn:de:kobv:b43-474832 |
URL: | https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 |
ISSN: | 1094-4087 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 05.03.2019 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 15.03.2019 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |