An imaging algorithm of planar array capacitance sensor for defect detection

Abstract

Planar array capacitance imaging is a feasible solution for detecting defects in composite components. However, the coplanar arrangement of electrodes in the imaging system renders a soft field effect, which results in unstable or susceptible imaging. The inverse problem of a planar array capacitance sensor system is ill-posed. Hence, a wavelet fusion combined multi-objective threshold programming imaging algorithm is proposed for a planar array capacitive imaging system. Furthermore, initial values of conjugate gradient (CG) and Newton–Raphson (NR) algorithms are optimized using the Tikhonov regularization algorithm. Subsequently, wavelet fusion is introduced to fuse images obtained using the CG and NR algorithms to acquire more detailed information. To further improve the reconstructed quality, a multi-objective threshold programming strategy is proposed. The final reconstruction image is obtained using the optimal threshold. Experimental results show a significantly improved quality of the reconstructed image, thereby verifying the effectiveness of the presented algorithm.