Research on acoustic emission monitoring of mechanical behavior of differentiated aging composites based on improved Laplacian score feature selection algorithm

As a fundamental material in the aerospace sector, the long-term service performance of composites is inseparable from their internal damage evolution. Therefore, studying the relationship between damage evolution and the mechanical behavior of composites in different aging states is of great significance. Acoustic emission (AE) has emerged as an effective method for investigating the damage evolution of composites due to its high sensitivity to material microscopic damage and the superiority of real-time monitoring, and the key to studying damage evolution lies in the accurate selection of AE features. The Laplacian score (LS) is a traditional algorithm for feature selection in AE, but its limitations severely hinder the application of the algorithm. In this study, an improved LS feature selection algorithm (Imp-LS) is proposed, which enhances the ability to identify feature correlations and improves the robustness of feature selection. The robustness of the Imp-LS algorithm and the effectiveness of AE for whole-life health monitoring of composites are validated through monitoring the mechanical behavior of double cantilever beam testing on various aging composites.