Simultaneous Temperature and Strain Measurement in Fiber Bragg Grating via Wavelength-Swept Laser and Machine Learning

Measuring temperature and strain using fiber Bragg grating (FBG) simultaneously has been a difficult task in recent years. Creating a dataset to differentiate temperature and strain via machine learning (ML) techniques necessitates collecting voluminous data rapidly. This study proposes a method to simultaneously measure the temperature and strain of a single FBG using ML while employing a wavelength-stabilized wavelength-swept laser (WSL). We analyzed peak intensities and sidelobe positions in the temporal domain under different temperatures (
$0~^{\circ }$
C–
$55~^{\circ }$
C) and strains (0–
$300~\mu \varepsilon $
) of FBG, amassing approximately 46000 datasets comprising six variables. ML was performed using Python’s Scikit-learn, with the dataset split into training (75%) and testing (25%) subsets. Temperature and strain could be measured simultaneously with approximately 95% accuracy, suggesting the potential of ML in accurately predicting temperature and strain using FBG responses.