Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study

This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SHM). A self-referencing setup, employing FBG sensors with edge filtering method and remote bonding, enables a baseline-free damage detection approach. The methodology is validated as a proof-of-concept numerical model. The simulation framework incorporates a three-dimensional spectral element method for precise and efficient modelling of GW propagation and interactions with structural anomalies. Three different machine learning (ML) techniques are employed to detect and localize damages, demonstrating effectiveness of ML methods compared to traditional methods. The three techniques employed are decision tree, logistic model tree and random forest. Key findings highlight the effectiveness of random forest models in classifying damage states with a 98.67% accuracy. Different feature selection methods, are used to identify critical features. The proposed methodology reduces sensor requirements, lowers system complexity and cost, and enables efficient SHM solutions in extreme or large-scale environments. This work underscores the potential of ML techniques to perform detection and localization where traditional techniques fail.