Intelligent Damage Identification and Prognosis for Composite Structures

Based on a series of successful Phase I demonstrations, Impact Technologies, in collaboration with the University of Dayton Research Institute (UDRI) and GE Aircraft Engines, propose to further develop and implement an autonomous system for detecting, localizing and predicting the extent of damage associated with aerostructure impacts. Specifically, this proposed Phase II program will consists of: 1) Implementing a reliable, accelerometer-based sensor system used for application on a GEAE engine casing; 2) Developing and deploying advanced, time-frequency, feature extraction techniques using wavelet packets and higher order statistical moments used for identifying the location and severity of impact responses; 3) Implementing a feature-based approach for damage isolation and severity prediction using neural networks and triangulation techniques; 4) Developing experimental strain wave analysis and optimized sensor triangulation procedure; 5) Developing and integrating dispersive wave propagation models that can be fused with the feature-based estimates to derive the real-time estimates of damage severity; and 6) Performing a series of well-organized tests with various levels of external and internal damage and implement and evaluate the developed approaches. The program focus will be on implementing a fieldable system that incorporates these developments for GEAE engine casings and other subcomponents.