Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
561732 | Mechanical Systems and Signal Processing | 2010 | 11 Pages |
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] and [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.