Wavelet-Integrated Alternating Sparse Dictionary Matrix Decomposition in Thermal Imaging CFRP Defect Detection

With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP during the manufacturing process as well as the in-service procedure. The most common types of defects in the CFRP are debonds and delaminations. It is difficult to detect the inner defects on a complex-shaped specimen using conventional nondestructive testing (NDT) methods. In this paper, an unsupervised machine learning method based on wavelet-integrated alternating sparse dictionary matrix decomposition is proposed to extract the weaker and deeper defect information for CFRP by using the optical pulse thermography (OPT) system. We propose to model the low-rank and sparse decomposition jointly in an alternating manner. By incorporating the low-rank information into the sparse matrix and vice versa, the weaker defects will be more efficiently extracted from noise and background. In addition, the integration of wavelet analysis with dictionary factorization enables an efficient time-frequency mining of information and significantly removes the high frequency noise as well as boosts the speed of computations. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular-and irregular-shaped CFRP specimens. A comparative analysis has also been undertaken to study the proposed method against the general OPTNDT methods. The MATLAB demo code can be linked: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index. htm.