采用高双折射九孔光纤的扭矩传感器

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-07-03 DOI:10.1109/JSEN.2025.3577300

Armando Rodriguez;Jose R. Vento Alvarez;Marko Galarza;Evelyn Vanegas-Tenezaca;Kay Schuster;Mikel Bravo;Manuel Lopez-Amo

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引用次数: 0

摘要

本文提出了一种新型的高双折射光纤扭矩传感器，该传感器基于嵌入Sagnac干涉仪（SI）的九孔七芯微结构光纤（MHCF）。该光纤的一段插入到对称的SMF-MMF-MHCF-MMF-SMF排列中，这为双折射光纤的多芯提供了有效的耦合，从而实现了多模干涉（MMI）。采用快速傅里叶变换（FFT）光谱数据分析提高了测量稳定性，减少了对光源变化的依赖。该传感器对- 50°和+50°之间的扭转角具有线性响应，灵敏度为16 mrad/°。通过机器学习技术的应用，提高了传感器的高灵敏度和良好的线性度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Torsion Sensor Using a High-Birefringence Nine-Hole Optical Fiber

This article presents a novel high-birefringence fiber-based torsion sensor based on a microstructured optical fiber with nine holes and seven cores microstructured holes and cores optical fiber (MHCF) embedded into a Sagnac interferometer (SI). A segment of this fiber is inserted into a symmetric SMF-MMF-MHCF-MMF-SMF arrangement, which provides efficient coupling to the multiple cores of the birefringent fiber and, consequently, multimode interference (MMI). Fast Fourier transform (FFT) spectral data analysis is employed to enhance measurement stability and reduce dependence on optical source variations. The sensor demonstrates a linear response to torsion angles between −50° and +50°, with a 16-mrad/° sensitivity. The high sensitivity and good linearity of the sensor are enhanced through the application of machine learning (ML) techniques.

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice