Abstract
Time series prediction algorithms combined with ultrasonic chaotic excitations have shown the ability to locate and identify loss of preload in a bolted aluminum joint in previous research [1,2]. This study examines the ability of this method to classify various bond state damage conditions of a composite bonded joint, including various disbond sizes and poorly cured bonds. The stiffened panel test structure is intended to be a simplification of a wing skin-to-spar bonded joint. An active excitation signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the bonded joint and sensed using an equivalent MFC patch on the opposite side of the joint. There is an MFC actuator/sensor pair for each bond condition to be identified. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence.
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