Time-of-flight-based guided-wave imaging for multi-damage localization in large plate structures with limited sensing paths

Guided-wave imaging is widely used for structural health monitoring and non-destructive evaluation of plate-like structures; however, many existing imaging methods require dense sensing networks and a large number of sensing paths, increasing data acquisition effort, storage requirements, and computational cost. This study proposes a time-of-flight-based guided-wave imaging framework for blind multi-damage localization in isotropic aluminum plates using a limited number of sensing paths. A pulse–echo sensing configuration with eight sensing paths is employed, and baseline-subtracted residual signals are processed using continuous wavelet transform to extract time-of-flight-related scattering features. An automated peak-selection pipeline, combining adaptive local-statistics thresholding, peak suppression, and distance-conditioned amplitude weighting, is introduced to identify reliable damage-reflection candidates without prior knowledge of the number or locations of damages. The selected features are then fused using a time-of-flight-based probabilistic imaging formulation to reconstruct damage index maps. Finite element simulations and plate experiments are conducted for one to three defects, including cases with a defect located outside the sensor-network region. Numerical results show relative localization errors of 1.95% for one defect, 1.19% and 5.92% for two defects, and 1.58%, 1.83%, and 0.96% for three defects. Experimental validation gives corresponding errors of 1.35%, 1.02% and 3.78%, and 1.48%, 4.27%, and 3.62%. The results demonstrate that blind multi-damage localization is feasible using only eight sensing paths when automated time-of-flight feature extraction and probabilistic fusion are combined. The proposed framework reduces sensing-path requirements while maintaining acceptable localization accuracy, although further validation is needed for very closely spaced damages and more complex operational conditions.