Development of artificial neural networks for real time and high resolution strain sensing
- We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach. This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest.
Autor*innen: | Christos Karapanagiotis |
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Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2018 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik | |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Distributed fiber-optic sensors; Dynamic strain sensing; Machine learning |
Themenfelder/Aktivitätsfelder der BAM: | Umwelt |
Umwelt / Sensorik | |
Veranstaltung: | Workshop (in collaboration with KIT) |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 27.08.2018 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 24.03.2021 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |