Acousto-ultrasonic health monitoring of composite materials and geometrically complex systems, such as a full-scale air vehicle, is a very complex process In full-scale systems, the acoustic wave travels through several interconnected and acoustically mismatched pieces such as sandwich core panels, stringer stiffened skins, lugs, and holes at various velocities while undoing multiple reflections, refractions, and mode changes before reaching a surface mounted transducer Existing techniques for acoustic emission (AE) source location and severity analysis rely on the assumption of constant wave speed and uninterrupted propagation path between sources and sensors. In complex full-scale systems this assumption, however is not valid. The goal of this paper is to present a distributed data centric acoustic emission sensor network and data processing algorithm for in-situ SHM of a full-scale air vehicle. The AE sensor network uses several lightweight direct-wave and network-fence sensors as well as various acquisition control time parameters to filter noise and minimize effects of cascading, reflection, and signal distortion. Source location is performed using a time-offset method with the assumption of material quasi-isotropy. Results from case study involving a full-scale composite fuselage instrumented with multiple AE sensors have shown that AE based SHM in geometrically complex and full-scale systems, despite exceedingly challenging, can be performed with reasonable accuracy using networked sensors and a series of adaptive signal acquisition control parameters.
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