Multidefect Joint Image Reconstruction Method of Carbon Fiber Composite Cable in Electromagnetic Tomography

To address the challenge of multi-defect electromagnetic tomography imaging in carbon fiber composite cables, this study proposes an innovative multi-defect joint reconstruction method combining pattern matching with compressed sensing techniques. First, twelve single-defect finite element models are constructed based on the electromagnetic properties of carbon fiber composite cables to analyze the induced voltage responses. The cross-sectional sensitivity matrix is derived by simulations of perturbations in the anisotropic carbon fiber composite medium. Next, the multi-defect induced voltage signals are decomposed into component signals using a pattern matching method based on normalized sparse representation. Finally, the component signals are reconstructed locally and sparsely using Compressive Sampling Matching Pursuit reconstruction algorithm. To balance computational efficiency and reconstruction accuracy, the optimal parameters are chosen as 12 iterations with a sparsity of 10. It is shown that the proposed joint multi-defect reconstruction method effectively suppresses artifacts at the edges of the imaging domain and increases the correlation coefficient by 0.16, 0.20, and 0.32 compared with the LBP, Tikhonov regularization, and CoSaMP methods, respectively. The proposed multi-defect joint reconstruction method offers a viable solution for multi-defect imaging of carbon fiber composite cables.