Fiber Bragg grating sensor fatigue crack real-time monitoring based on spectrum cross-correlation analysis

Bao, Pengyu; Yuan, Mei; Dong, Shaopeng; Hao, Song; Xue, Jingfeng

doi:10.1016/j.jsv.2012.07.049

Cited by 28 publications

(12 citation statements)

References 16 publications

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“…That effort carried out on other applications that are being investigated for the National Aeronautics and Space Administration's Space Station [5]. Some of the most widely used technologies in this field are: fastener monitoring [30], blade tip clearance [31], and fatigue monitoring [32]. The Civil Engineering community has researched on vibration-based Damage Identification of bridges and buildings since the 80 s [33,34].…”

Section: History and Evolutionmentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

220

124

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Section: History and Evolutionmentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

220

124

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“…In SHM applications, data acquisition system plays an important role. Compared with conventional sensors, FBG sensor has many advantages such as their small size, corrosion resistance, immunity to electromagnetic interference, and good compatibility with many composite materials [5][6][7][8]. Hence, in our work, FBG sensors have been selected and buried in the composite plate to collect signal and monitor structure performance.…”

Section: Introductionmentioning

confidence: 99%

Impact Localization Method for Composite Plate Based on Low Sampling Rate Embedded Fiber Bragg Grating Sensors

Zhuo

Yuan

Hao

et al. 2017

Mathematical Problems in Engineering

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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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“…As the laminates were loaded in tension, the FWHM showed a strong correlation to the transverse crack density. Yuan [14] applied a wavelet analysis theory to extract two damage factors, the crack initiation factor (CIF) and the crack propagation factor (CPF), for damage initiation and propagation degree identification. Bernasconi [15] investigated experimentally a fatigue crack growth in adhesively-bonded joints of thick composite laminates by employing an array of FBG sensors.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Model Updating for Sizing of Hole-Edge Crack Using Fiber Bragg Grating Sensors and the High-Order Extended Finite Element Method

Yang

Wang

et al. 2016

Sensors

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This paper presents a novel framework for probabilistic crack size quantification using fiber Bragg grating (FBG) sensors. The key idea is to use a high-order extended finite element method (XFEM) together with a transfer (T)-matrix method to analyze the reflection intensity spectra of FBG sensors, for various crack sizes. Compared with the standard FEM, the XFEM offers two superior capabilities: (i) a more accurate representation of fields in the vicinity of the crack tip singularity and (ii) alleviation of the need for costly re-meshing as the crack size changes. Apart from the classical four-term asymptotic enrichment functions in XFEM, we also propose to incorporate higher-order functions, aiming to further improve the accuracy of strain fields upon which the reflection intensity spectra are based. The wavelength of the reflection intensity spectra is extracted as a damage sensitive quantity, and a baseline model with five parameters is established to quantify its correlation with the crack size. In order to test the feasibility of the predictive model, we design FBG sensor-based experiments to detect fatigue crack growth in structures. Furthermore, a Bayesian method is proposed to update the parameters of the baseline model using only a few available experimental data points (wavelength versus crack size) measured by one of the FBG sensors and an optical microscope, respectively. Given the remaining data points of wavelengths, even measured by FBG sensors at different positions, the updated model is shown to give crack size predictions that match well with the experimental observations.

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Fiber Bragg grating sensor fatigue crack real-time monitoring based on spectrum cross-correlation analysis

Cited by 28 publications

References 16 publications

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Impact Localization Method for Composite Plate Based on Low Sampling Rate Embedded Fiber Bragg Grating Sensors

Probabilistic Model Updating for Sizing of Hole-Edge Crack Using Fiber Bragg Grating Sensors and the High-Order Extended Finite Element Method

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