Resolving Predicament Related to the Hysteresis and Temperature Effect in FBG-Based Current Sensors by Utilizing Machine Learning

Sanjib Sarkar; Mandrita Banerjee; Mehdi Tarhani; Morad Khosravi Eghbal; Mehdi Shadaram

doi:10.1364/SENSORS.2020.STu4D.4

Optical Sensors and Sensing Congress
OSA Technical Digest (Optica Publishing Group, 2020),
paper STu4D.4
•https://doi.org/10.1364/SENSORS.2020.STu4D.4

Resolving Predicament Related to the Hysteresis and Temperature Effect in FBG-Based Current Sensors by Utilizing Machine Learning

Sanjib Sarkar, Mandrita Banerjee, Mehdi Tarhani, Morad Khosravi Eghbal, and Mehdi Shadaram

Optical Sensors 2020

Washington, DC United States
22–26 June 2020
ISBN: 978-1-943580-78-1

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Optical Fiber Sensors for Harsh Environment I (STu4D)

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S. Sarkar, M. Banerjee, M. Tarhani, M. K. Eghbal, and M. Shadaram, "Resolving Predicament Related to the Hysteresis and Temperature Effect in FBG-Based Current Sensors by Utilizing Machine Learning," in Optical Sensors and Sensing Congress, OSA Technical Digest (Optica Publishing Group, 2020), paper STu4D.4.

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Abstract

This paper presents machine learning application to compensate for hysteresis and temperature effects in magnetostrictive-Fiber Bragg grating (FBG)-based current sensors. The proposed technique will reduce complexity and cost of FBG-based current sensors.

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