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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/52583 |
| Acceso en línea: | https://hdl.handle.net/2454/52583 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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 |
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