Damage Detection in Composite Materials Using Hyperspectral Imaging

Vision based techniques are successfully applied in for Structural Health Monitoring (SHM). Between them one can distinguish thermography, video-based methods and hyperspectral imaging method. Hyperspectral Imaging (HSI) is a method of obtaining an array of two-dimensional images over a wide range of wavelengths in the electromagnetic spectrum. HSI has found its applications in the fields of geographic remote sensing, food quality inspection, vegetation monitoring and medicine. One of the areas in which HSI usage is still developing is Structural Health Monitoring. There is a big potential of the HSI application because during hyperspectral imaging we can detect changes in physical and chemical properties of materials under the test. The aim of the study was to develop a technique for damage detection in glass fibre reinforced polymer composites, which is relatively difficult to achieve with other optical methods. The hyperspectral images for healthy and damaged composite samples are compared. For detection of damages, two approaches were investigated; a target detection algorithm using spectral data as the detection criterion, and an algorithm based on cointegration analysis. The strengths and weaknesses of both approaches are compared, and their applicability for SHM is assessed.

Automated summary

Vision based techniques, including thermography, video-based methods and hyperspectral imaging method, have been successfully applied in Structural Health Monitoring (SHM). Hyperspectral Imaging (HSI), as a method of obtaining an array of two-dimensional images over a wide range of wavelengths, has found applications in various fields. This study aims to develop a technique for damage detection in glass fibre reinforced polymer composites using HSI, and compares two different approaches for damage detection.