Inverse finite element method and support vector regression for automated crack detection with OFDR-Distributed fiber optic sensors

Abstract

Crack detection is a crucial but challenging task. In this paper, a new method is introduced for automatical detection of the substrate cracks. An anomaly index is defined based on optical frequency domain reflectometry (OFDR) measured strain and inverse finite element method (iFEM) recovered strain. The index is sensitive to crack-induced strain but not sensitive to substrate strain. Leveraging on the self-defined anomaly index, the crack location can be identified with millimetre accuracy. Then, the detection of the crack depth is regarded as a typical multi-regression analysis and solved by support vector regression (SVR) method. Particle swarm optimization (PSO) approach is applied to find optimal parameters of the SVR kernel function. Experimental cases of a simple-supported beams with different crack locations and depths are performed, demonstrating the excellent prediction accuracy of the method. The proposed method has a promising potential in identification and quantification of cracks.