Big structures monitoring is a relevant issue due to their size and trouble to find damages. The goal of this thesis is the achievement of a discrimination between temperature and strain effects occurring on a fiber. The use of distributed OFSs is of big help: the work is based on a BOTDA configuration to retrieve the BGSs along the FUT and use ANNs to analyze them. A maximum of around 70% in some cases for strain and temperature and a unique 86.7% of success for strain selection are reached
Discrimination of strain and temperature in Brillouin Optical Time Domain Analyzers via Artificial Neural Networks
Piccolo, Arianna
2016/2017
Abstract
Big structures monitoring is a relevant issue due to their size and trouble to find damages. The goal of this thesis is the achievement of a discrimination between temperature and strain effects occurring on a fiber. The use of distributed OFSs is of big help: the work is based on a BOTDA configuration to retrieve the BGSs along the FUT and use ANNs to analyze them. A maximum of around 70% in some cases for strain and temperature and a unique 86.7% of success for strain selection are reachedFile in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
piccolo_arianna_tesi.pdf
accesso aperto
Dimensione
1.33 MB
Formato
Adobe PDF
|
1.33 MB | Adobe PDF | Visualizza/Apri |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/27521