Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks

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URI: http://hdl.handle.net/10902/15663
ISSN: 0733-8724
ISSN: 1558-2213
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Autoría
Ruiz Lombera, RubénAutoridad Unican; Fuentes Cayón, Alberto; Rodríguez Cobo, LuisAutoridad Unican; López Higuera, José MiguelAutoridad Unican; Mirapeix Serrano, Jesús MaríaAutoridad Unican
Fecha
2018-06-01
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© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publicado en
Journal of Lightwave Technology, 2018, 36(11), 2114-2121
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IEEE-
The Optical Society
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Palabras clave
Artifical neural network
Distributed systems
Optical fiber sensors
Stimulated Brillouin scattering
Strain-temperature discrimination
Resumen/Abstract
A system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without compromising the complexity or cost of the interrogation unit. The classification results, achieved by considering a preprocessing stage with dimensionality reduction via principal component analysis and spatial filtering, improve those obtained in a previous feasibility study.
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