Depth detection of spar cap defects in large-scale wind turbine blades based on a 3D heat conduction model using step heating infrared thermography

Abstract

The defects dispersed in spar cap often lead to failure of large-scale wind turbine blades. To predict the residual service life of blade and make repair, it is necessary to detect the depth of spar cap defects. Step-heating thermography (SHT) is a common infrared technique in this domain. However, the existing methods of SHT on defect depth detection are generally based on 1D models, which are unable to accurately detect the depth of spar cap defects because of ignoring material anisotropy and in-plane heat flow. To improve the depth detection accuracy of spar cap defects, a 3D model based on the theory of heat transfer is established by using equivalent source method (ESM), and a defect depth criterion is proposed based on the analytical solution of heat conduction equation. The modeling process are as follows. The heat conduction model of SHT was established by ESM. Then, coordinate transformation, variables separation and Laplace transformation were utilized to solve the 3D heat conduction equation. A defect depth criterion was proposed based on emerging contrast Cr. A GFRP composites plate containing 12 square flat-bottom holes with different sizes and depths was manufactured to represent spar cap with large thermal resistance defects, such as Delamination and crack. Experimental results demonstrate the validity of 3D model. Then the model was applied to on-site SHT test of a 1.5 megawatts (MW) wind turbine blade. The test results prove that the depth detection accuracy of spar cap defects can be significantly improved by using 3D model. In addition, by using a improved principle component analysis (PCA) method containing contrast enhancement factor, artifacts can be reduced and the recognition time of defects can be shortened.

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