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A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

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Published on: September 30, 2019

Fiber directional position sensor based on multimode interference imaging and machine learning.

Kai Sun, Zhenming Ding, Ziyang Zhang

|July 2, 2020

Summary

This summary is machine-generated.

This study introduces a novel fiber optic position sensor using multimode interference and machine learning. The system accurately detects fiber distortions for precise directional positioning, achieving over 97% accuracy.

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Last Updated: Dec 16, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

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Published on: November 7, 2016

Implementation of a Reference Interferometer for Nanodetection

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Published on: April 26, 2014

Area of Science:

Optoelectronics
Machine Learning Applications
Fiber Optic Sensing

Background:

Fiber optic sensors are crucial for various applications.
Detecting fiber distortions accurately is challenging.
Multimode interference patterns are sensitive to physical changes.

Purpose of the Study:

To develop a fiber optic directional position sensor.
To utilize multimode interference and machine learning for enhanced accuracy.
To investigate the sensor's performance in recognizing fiber positions.

Main Methods:

Single-mode light injection into multimode fiber to generate interference patterns.
Mounting fiber on an automatic translation stage for controlled movement.
Image capture using an infrared camera.
Machine learning model training, validation, and testing on captured images.

Main Results:

Achieved over 97% accuracy in recognizing fiber positions across four directions with 10 classes each (8 mm span).
Demonstrated over 60% accuracy at 5 µm resolution with a larger dataset.
The system's performance approaches the limit of the translation stage.

Conclusions:

The proposed sensor effectively uses multimode interference and machine learning for accurate fiber position sensing.
The system shows high potential for precise directional positioning applications.
Further dataset expansion could improve resolution and accuracy.