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Feasibility study of strain and temperature discrimination in a BOTDA system via artificial neural networks


Abstract: Automatic discrimination between strain and temperature in a Brillouin optical time domain analyzer via artificial neural networks is proposed and discussed in this paper. Using a standard monomode optical fiber as the sensing element, the ability of the proposed solution to detect the known changes that the Brillouin gain spectrum exhibits depending on the applied temperature and/or strain will be studied. Experimental results, where different simultaneous strain and temperature situations have been considered, will show the feasibility of this technique.



Otras comunicaciones del congreso o articulos relacionados con autores/as de la Universidad de Cantabria

 Autoría: Ruiz-Lombera R., Piccolo A., Rodriguez-Cobo L., Lopez-Higuera J., Mirapeix J.,

 Congreso: International Conference on Optical Fiber Sensors: OFS (25ª : 2017 : Jeju, República de Corea)

Editorial: SPIE Society of Photo-Optical Instrumentation Engineers

 Año de publicación: 2017

Nº de páginas: 4

Tipo de publicación: Comunicación a Congreso

DOI: 10.1117/12.2265435

ISSN: 0277-786X,1996-756X

Proyecto español: TEC2013-47264-C2-1-R ; TEC2016-76021-C2-2-R

Url de la publicación: https://doi.org/10.1117/12.2265435

Autores/as

RUBEN RUIZ LOMBERA

ARIANNA PICCOLO

LUIS RODRIGUEZ COBO