Kernel Feature Space Based Low Velocity Impact Monitoring

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient structural health monitoring methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this paper a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical feature matrices, composed of features extracted from the time and frequency domains, are developed. Kernel Principal Component Analysis (KPCA) is used to compress and classify the statistical feature matrices. Compared with traditional PCA algorithm, KPCA method shows better feature clustering and damage quantification capabilities. A new damage index, formulated using Mahalanobis distance, is defined to quantify impact damage. The developed methodology has been validated using low velocity impact experiments with a sandwich composite wing.