Probabilistic damage identification based on correlation analysis using guided wave signals in aluminum plates

An algorithm based on correlation analysis was adopted to estimate the probability of the presence of damage in aluminum plates using Lamb wave signals from an active sensor network. Both finite element analysis and experimental evaluations were presented. The Shannon entropy optimization criterion was applied to calibrate the optimal mother wavelet and the most appropriate continuous wavelet transform scale for signal processing. The correlation coefficients for individual sensing paths between the present state (with damage) and the reference state (without damage) were calculated, and the probability of the presence of damage in the monitoring area enclosed by the active sensor network was estimated to identify the damage. A concept of virtual sensing paths (VSPs) was proposed to enhance the performance of the algorithm by increasing the number of sensing paths in data fusion. The results identified using both simulation and experimental Lamb wave signals from different groups of sensing paths at different central frequencies agreed well with the actual situations, demonstrating the potential of the correlation-based algorithm with the application of VSPs for identification of damage in structures.