Machine Learning Methods for Discriminating Strain and Temperature Effects on FBG-Based Sensors

Abstract

The biggest challenge of using fiber Bragg grating (FBG) based sensors is the cross-sensitivity between the strain and temperature effects on FBG. In this letter, we demonstrate the ability of machine learning (ML) methods to discriminate between the strain and temperature effects on FBG sensors on a single measurement of change in the Bragg wavelength. Spectral data are collected using an FBG interrogation system at various strain and temperature conditions and are applied to different ML methods to determine the strain and temperature effects. We further simulate FBG with the same strain and temperature conditions using VPIphotonics. For comparison, the same ML methods are applied to both simulated and experimentally collected data. The experimental results reveal that our proposed model can predict strain and temperature with 90% accuracy on a single measurement of Bragg wavelength. We also demonstrate the stability of the model by comparing the testing and training errors of the applied ML methods. Therefore, our proposed technique reduces the cost and complexity associated with the existing FBG-based sensor system.