Chaotic insonification for health monitoring of an adhesively bonded composite stiffened panel

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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