TY - JOUR
T1 - Monte Carlo matching pursuit decomposition method for damage quantification in composite structures
AU - Das, S.
AU - Kyriakides, I.
AU - Chattopadhyay, A.
AU - Papandreou-Suppappola, A.
PY - 2009/4
Y1 - 2009/4
N2 - In wave-based approach, the presence of damage is visualized in terms of the changes in the signature of the resultant wave that propagates through the structure. In structural health monitoring, the fundamental goal is to detect, localize, and quantify these damage signatures. The current approach uses matching pursuit decomposition (MPD) to compare signals from healthy and damaged structures. However, the major drawback of the MPD is that, in the decomposition process, it performs an exhaustive search over a large dictionary of elementary functions. Therefore, this method of decomposition is associated with a large computational expense. In this research, the Monte Carlo matching pursuit decomposition (MCMPD) is proposed, that adapts a smaller dictionary to the signal structure, thus avoiding the exhaustive search over the time-frequency plane. The proposed algorithm, sequentially estimates a dictionary that contains only those components that match the waveform structure, uses the matching pursuits for the decomposition of the signal and if necessary, adapts the dictionary to the structure of the residues for further decomposition. Finally, we demonstrate using real life data that the MCMPD retains the ability of the matching pursuit to decompose waveforms and quantify them accurately while reducing computational expense.
AB - In wave-based approach, the presence of damage is visualized in terms of the changes in the signature of the resultant wave that propagates through the structure. In structural health monitoring, the fundamental goal is to detect, localize, and quantify these damage signatures. The current approach uses matching pursuit decomposition (MPD) to compare signals from healthy and damaged structures. However, the major drawback of the MPD is that, in the decomposition process, it performs an exhaustive search over a large dictionary of elementary functions. Therefore, this method of decomposition is associated with a large computational expense. In this research, the Monte Carlo matching pursuit decomposition (MCMPD) is proposed, that adapts a smaller dictionary to the signal structure, thus avoiding the exhaustive search over the time-frequency plane. The proposed algorithm, sequentially estimates a dictionary that contains only those components that match the waveform structure, uses the matching pursuits for the decomposition of the signal and if necessary, adapts the dictionary to the structure of the residues for further decomposition. Finally, we demonstrate using real life data that the MCMPD retains the ability of the matching pursuit to decompose waveforms and quantify them accurately while reducing computational expense.
KW - Fiber-reinforced composite
KW - Matching pursuit decomposition
KW - Monte carlo
KW - Particle filtering
KW - Structural health monitoring
KW - Wave propagation
UR - http://www.scopus.com/inward/record.url?scp=63849227125&partnerID=8YFLogxK
U2 - 10.1177/1045389X08097386
DO - 10.1177/1045389X08097386
M3 - Article
AN - SCOPUS:63849227125
SN - 1045-389X
VL - 20
SP - 647
EP - 658
JO - Journal of Intelligent Material Systems and Structures
JF - Journal of Intelligent Material Systems and Structures
IS - 6
ER -