Multiple damage identification of epoxy coating structures based on terahertz pulse imaging technology and machine learning

To enhance the diagnostic capabilities of defective epoxy coating structures using terahertz non-destructive testing technology, this paper proposed a multiple damage identification algorithm based on multi-domain feature fusion and machine learning. Three typical defect types of coating structure with three severity levels were investigated. Firstly, Finite-Difference Time-Domain (FDTD) modelling generated terahertz pulsed imaging (TPI) signals for various defective structures, incorporating ageing-induced property variations. Secondly, time domain, frequency domain and wavelet packet energy parameters were extracted, then filtered via the importance analysis using an improved random forest method to obtain the critical features sensitive to the defective structures. Subsequently, the selected features were reconstructed into optimised eigenvectors and processed through a cascading Support Vector Machines (SVM) classifier with Particle Swarm Optimisation (PSO) algorithm. Defect types classification and defect severity assessment were implemented through the cascading classifier. Compared to the traditional single-stage multiclass classifier, the proposed feature filtering algorithm significantly enhanced discriminative feature precision and better captured critical coating structural characteristics. This optimisation ultimately improved defect classification accuracy and reliability. The results indicated that the method was effective and could be recommended for the actual application.