Power over fiber system for heterogeneous sensors multiplexing

This paper presents a Power-over-Fiber based remote electronic and optical fiber sensors multiplexing scheme. The system architecture consists of a 50-km linear cavity Raman-fiber laser that is used for interrogation of FBG optical fiber sensors. Simultaneously, electronic sensors information is mod...

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Bibliographic Details
Authors: Rodríguez Rodríguez, Armando, Vanegas Tenezaca, Evelyn Dayanara, Vento Álvarez, José Raúl, López-Amo Sáinz, Manuel, Bravo Acha, Mikel
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Universidad Pública de Navarra
Repository:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/52583
Online Access:https://hdl.handle.net/2454/52583
Access Level:Open access
Keyword:Energy harvesting
Fiber Bragg grating
Modulator
Multiplexing
Optical fiber networks
Optical fiber sensors
Optical fibers
Optical sensors
Power over fiber
Sensors
Temperature measurement
Temperature sensors
Description
Summary:This paper presents a Power-over-Fiber based remote electronic and optical fiber sensors multiplexing scheme. The system architecture consists of a 50-km linear cavity Raman-fiber laser that is used for interrogation of FBG optical fiber sensors. Simultaneously, electronic sensors information is modulated in amplitude while the optical sensors' data are encoded in the spectral information. In order to bias the electronic sensors, the residual power of the Raman pump laser is collected in an energy harvesting unit. This electric power is used for biasing an ATTiny85 control unit and two electro-optical modulators. A proof-of-concept is presented where a couple of optical fiber-Bragg-gratings sensors collect strain information that is self-compensated in temperature according to the digital data achieved from the electronic sensors. A 9.6 kbit/s data rate was achieved using Mach-Zehnder amplitude modulators and a maximum 35 ksample/s was retrieved using a high-speed C-band spectrometer and performing spectral analysis via a software developed in Python. Authors