In recent decades, the FBG (Fiber Bragg Grating) sensors are widely used in SHM (Structural Health Monitoring) fields for the characteristics of its simple structure, tiny volume, light weight, anti-corrosion, and anti-electromagnetic interference. However, there are two defects that limit its application in some fields. Firstly, the low sampling frequency due to the induction principle provides a big challenge to the solver algorithm. Secondly, real-time online monitoring which requires quick and efficient signal processing for the long time series is the development direction of SHM and also needs progress in the fast solver algorithm. For these two reasons, we propose a novel method based on the PSD (Power Spectrum Density) to analyze the sampled data from FBG sensors in this paper and it is applied in an online crack initiation monitoring system and obtain some satisfactory results. A comparison with the preliminary research indicates the accuracy and rapidity of the proposed algorithm and points out the applicability in the real-time online monitoring system for crack initiation.
With aircraft structural safety becomes an increasingly issue, people start to use Structural Health Monitoring (SHM) technology to monitor the reliability of airframe structural materials. Fiber Bragg Grating (FBG) sensors are often used to monitor the composite materials due to their inherent advantages, but the gap between the FBG sensors' sampling rate and the damage monitoring signals' bandwidth has brought problem analyzing the 'health condition' of the airframe structure. To solve this problem, SHM technology, in conjunction with the reconstruction algorithms of Compressed Sensing (CS) theory, is expected to compensate the losing information of the signals sampled by FBG sensors and reconstruct the high frequency damage monitoring signals. In order to satisfy the applicable conditions of CS, this paper proposes an innovative method to convert a 1D signal to a 2D (2D) signal and has designed corresponding structurally random measurement matrix. Finally, the high frequency damage monitoring signal is reconstructed successfully and the relative error of the reconstruction is less than 30% under appropriate number of samples.
Fiber Bragg Grating (FBG) sensors have been increasingly used in the field of Structural Health Monitoring (SHM) in recent years. In this paper, we proposed an impact localization algorithm based on the Empirical Mode Decomposition (EMD) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) to achieve better localization accuracy for the FBG-embedded plate. In our method, EMD is used to extract the features of FBG signals, and PSO-SVM is then applied to automatically train a classification model for the impact localization. Meanwhile, an impact monitoring system for the FBG-embedded composites has been established to actually validate our algorithm. Moreover, the relationship between the localization accuracy and the distance from impact to the nearest sensor has also been studied. Results suggest that the localization accuracy keeps increasing and is satisfactory, ranging from 93.89% to 97.14%, on our experimental conditions with the decrease of the distance. This article reports an effective and easy-implementing method for FBG signal processing on SHM systems of the composites.
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