Damage detection in glass-fibre reinforced polymer (GFRP) structures, such as blades of wind turbines, is a challenging task to achieve using most of conventional methods used in Structural Health Monitoring (SHM). The primary cause of this issue is the relatively high internal damping of the material. Vision based methods however circumvent this issue. Among those methods hyperspectral imaging (HSI), a technique in which an image is recorded in a broad spectrum of electromagnetic radiation, has been proven to be a valuable tool for this purpose. Because of the high spectral resolution, hyperspectral images contain information about the chemical composition of the object being scanned. In this study, the chemical data contained in the hyperspectral images of GFRP samples is used as a basis for detection of presence of moisture-related damages. The aim of this study is to develop an algorithm allowing for detection of moisture-related damage in GFRP structures. The algorithm utilizes the interaction of light with moisture through the phenomenon of absorption, cointegration analysis as a denoising and detrending tool, and machine learning methods for the purpose of classification. The results of proposed algorithm are evaluated and its applicability for the purpose of SHM is assessed.
KEYWORDS: Wavelets, Data acquisition, Damage detection, Wave plates, Signal processing, Aluminum, Fractal analysis, Structural health monitoring, Wavelet transforms, Temperature metrology
The paper demonstrates how to remove the undesired temperature effect from Lamb wave data in order to detect
structural damage more precisely and reliably. The method used is based on the cointegration technique and wavelet
analysis. The former is built on the analysis of non-stationary behaviour whereas the latter brings the concept of multiresolution
decomposition of time series. Instead of directly using Lamb wave data for damage detection, three
approaches are used: (1) analysis of the variance of wavelet coefficients of Lamb wave responses before cointegration,
(2) analysis of the cointegrating residuals obtained from the cointegration process of Lamb wave responses, and (3)
analysis of the variance of wavelet coefficients of Lamb wave responses after cointegration. These approaches are tested
on undamaged and damaged aluminium plates that have been exposed to temperature variations. The experimental
results show that the first approach still exhibits temperature variability and damage cannot be detected. In contrast the
second and third approaches can isolate damage-sensitive features from temperature variations, detect the existence of
damage and classify its severity.
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