Defect identification using sampling and outlier analysis in passive guided wave structural health monitoring

Passive Structural Health Monitoring (SHM) systems face a challenge in achieving control over the propagating noise field, thereby limiting their application towards defect identification and localization. To overcome this, an outlier statistical technique based on cross-correlation is developed and implemented here to achieve defect detection under passive loading. The proposed technique is first demonstrated using simulation and then validated using an experimental setup based on an air blower as the noise source imposing on an aluminium plate. The results clearly demonstrate the feasibility of defect detection using the sparse sampling-based outlier analysis in a passive SHM system that can effectively assess the structural health integrity of infrastructures deployed under harsh environmental conditions.