Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
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Ruiz Lombera, Rubén; Fuentes Cayón, Alberto; Rodríguez Cobo, Luis; López Higuera, José Miguel; Mirapeix Serrano, Jesús MaríaFecha
2018-06-01Derechos
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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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