Effect of Peak Tracking Methods on FBG Calibration Derived by Factorial Design of Experiment.

Nazila Safari Yazd,Patrice Mégret,Jennifer Kawakami,Alireza Izaddoost

doi:10.3390/s21186169

Nazila Safari Yazd, Patrice Mégret + Show 2 more

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https://doi.org/10.3390/s21186169

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Abstract

We present a calibration procedure for a humidity sensor made of a fiber Bragg grating covered by a polyimide layer. FBGs being intrinsically sensitive to temperature and strain, the calibration should tackle three variables, and, therefore, consists of a three-variable, two-level factorial design tailored to assess the three main sensitivities, as well as the five cross-sensitivities. FBG sensing information is encoded in the reflection spectrum from which the Bragg wavelength should be extracted. We tested six classical peak tracking methods on the results of the factorial design of the experiment applied to a homemade FBG humidity sensor. We used Python programming to compute, from the raw spectral data with six typical peak search algorithms, the temperature, strain and humidity sensitivities, as well as the cross-sensitivities, and showed that results are consistent for all algorithms, provided that the points selected to make the computation are correctly chosen. The best results for this particular sensor are obtained with a 3 threshold, whatever the peak search method used, and allow to compute the effective humidity sensitivity taking into account the combined effect of temperature and strain. The calibration procedure presented here is nevertheless generic and can thus be adapted to other sensors.

Highlights

Fiber Bragg grating (FBG) is a periodic and permanent modulation of the refractive index of the optical fiber core, which is achieved by exposing the optical fiber core to the interference pattern of ultraviolet light [1,2,3,4,5]
The calibration of FBG sensors is vital to making good measurements
This is especially true when the sensor is sensitive to multiple parameters, as the cross-sensitivities affect the effective sensitivity of the main variables. To conduct this calibration in an efficient way, a factorial design with a first-order model with interaction is first tested, and if the control point agrees with the model, the procedure stops there

Summary

Introduction

Fiber Bragg grating (FBG) is a periodic and permanent modulation of the refractive index of the optical fiber core, which is achieved by exposing the optical fiber core to the interference pattern of ultraviolet light [1,2,3,4,5].When light propagates through an FBG, a specific wavelength, referred to as the Bragg wavelength λB, is reflected in phase by each grating plane, whereas the remaining wavelengths pass through it. The Bragg wavelength depends on the effective refractive index of the core and the grating period Both parameters are inherently temperature and axial strain-dependent. Any modification in temperature or strain causes the Bragg wavelength to change, and this property is the basis for an FBG sensor: by monitoring the Bragg wavelength, strain or temperature can be monitored. FBG-based sensors are lightweight, small-sized, and passive They are immune to electromagnetic interference as well. FBGs have the potential for quasi-distributed sensing They have been used to monitor temperature, humidity, strain, external refractive index, and bending [11,12,13,14,15,16,17,18,19]. FBG-based sensors can be operated in harsh environments with severe physical/chemical conditions such as very high temperature, and high pressure [20,21,22,23,24,25,26]

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Discussion

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