The aerospace field has concentrated substantial attention toward the development of structural health monitoring (SHM) systems for multidisciplinary applications. Research is motivated by catastrophic failures of operational systems which may have been avoided with the prior implementation of a successful damage prediction method. This research utilizes a smart sensor array to collect sensing information over a variety of damaged scenarios on a composite lap-joint assembly. Damage was implemented as bolt torque loss within the joint. A damage index was used as the key diagnostic feature to interrogate damage within the structure. Pattern recognition of the damage index, in addition to a rule-based, statistical discrimination method was employed to detect, localize and quantify damage due to bolt torque loss in the structure. The methodology accurately detected the presence of damage within the joint, localized the damage within the structures four quadrants, and assessed the level of torque loss.
The aerospace field has concentrated substantial attention toward the development of structural health monitoring (SHM) systems for multidisciplinary applications. Research is motivated by catastrophic failures of operational systems which may have been avoided with the prior implementation of a successful damage prediction method. This research utilizes a smart sensor array to collect sensing information over a variety of damaged scenarios on a composite lap-joint assembly. Damage was implemented as bolt torque loss within the joint. A damage index was used as the key diagnostic feature to interrogate damage within the structure. Pattern recognition of the damage index, in addition to a rule-based, statistical discrimination method was employed to detect, localize and quantify damage due to bolt torque loss in the structure. The methodology accurately detected the presence of damage within the joint, localized the damage within the structures four quadrants, and assessed the level of torque loss.
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