Open AccessStudy of sensitivity for strain-based structural health monitoring
Author(s) -
A. Herrera-Iriarte,
Joham Alvarez-Montoya,
Julián Sierra-Pérez
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/836/1/012001
Subject(s) - structural health monitoring , sensitivity (control systems) , fiber bragg grating , beam (structure) , strain (injury) , optical fiber , computer science , fiber optic sensor , event (particle physics) , materials science , field (mathematics) , structural engineering , acoustics , electronic engineering , engineering , physics , telecommunications , medicine , mathematics , quantum mechanics , pure mathematics
One of the available methodologies for structural health monitoring (SHM) is based on strain field pattern recognition where, through the use of sensors capable of measuring strain on discrete points and machine learning techniques, it is possible to detect a damage event. In this study, strain data from fiber optic sensors (FOS), in particular fiber Bragg gratings (FBG), acquired through two experiments are used: an aluminum beam with 32 FBGs and CFRP beam provided with 20 FBGs, which serves as the main wing’s structure of an unmanned aerial vehicle (UAV). Both structures were subjected to dynamic loading for a pristine condition and later, for artificially damaged conditions. In the experiments presented in this paper the beams were provided with different amounts of sensors which were removed one by one in order to analyze the sensitivity of the damage detection methodology based on PCA to a change in the number of sensors. The results demonstrated that there are few sensors that contribute mostly to the methodology’s performance, these sensors are validated to be the ones located near the analyzed damage condition. Therefore, this study is the first step into the development of methodologies of damage localization using strains.