TY - GEN
T1 - Kernel feature space based low velocity impact monitoring
AU - Liu, Yingtao
AU - Yekani Fard, Masoud
AU - Chattopadhyay, Aditi
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84892640610&partnerID=8YFLogxK
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U2 - 10.1115/SMASIS2012-8242
DO - 10.1115/SMASIS2012-8242
M3 - Conference contribution
AN - SCOPUS:84892640610
SN - 9780791845097
T3 - ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2012
SP - 917
EP - 924
BT - ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2012
T2 - ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2012
Y2 - 19 September 2012 through 21 September 2012
ER -