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Passive sensing technique may eliminate the need of expending power from actuators and thus provide a means of developing a compact and simple structural health monitoring system. More importantly, it may provide a solution for monitoring the aircraft subjected to environmental loading from air flow during operation. In this paper, a non-contact auto-correlation based technique is exploited as a feasibility study for passive sensing application to detect damage and isolate the damage location. Its theoretical basis bears some resemblance to reconstructing Green’s function from diffusive wavefield through cross-correlation. Localized high pressure air from air compressor are randomly and continuously applied on the one side surface of the aluminum panels through the air blow gun. A laser Doppler vibrometer (LDV) was used to scan a 90 mm × 90 mm area to create a 6 × 6 2D-array signals from the opposite side of the panels. The scanned signals were auto-correlated to reconstruct a “selfimpulse response” (or Green’s function). The premise for stably reconstructing the accurate Green’s function requires long sensing times. For a 609.6 mm × 609.6 mm flat aluminum panel, the sensing times roughly at least four seconds is sufficient to establish converged Green’s function through correlation. For the integral stiffened aluminum panel, the geometrical features of the panel expedite the formation of the diffusive wavefield and thus shorten the sensing times. The damage is simulated by gluing a magnet onto the panels. Reconstructed Green’s functions (RGFs) are used for damage detection and damage isolation based on an imaging condition with mean square deviation of the RGFs from the pristine and the damaged structure and the results are shown in color maps. The auto-correlation based technique is shown to consistently detect the simulated damage, image and isolate the damage in the structure subjected to high pressure air excitation. This technique may be transformed into passive sensing applied on the aircraft during operation.
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