TY - GEN
T1 - Active learning data selection for adaptive online structural damage estimation
AU - Chakraborty, Debejyo
AU - Kovvali, Narayan
AU - Papandreou-Suppappola, Antonia
AU - Chattopadhyay, Aditi
PY - 2010
Y1 - 2010
N2 - Adaptive learning techniques have recently been considered for structural health monitoring applications due to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of environmental and operating conditions. In this paper, an active learning data selection procedure is proposed that adaptively selects the most informative measurements to include, from multiple available measurements, in estimating structural damage. This is important, since not all the measurements may provide useful information and could reduce performance when processed. Within the adaptive learning framework, the data selection problem is formulated to choose those measurements which are most representative of the diversity within a damage state. This is achieved by extracting time-frequency analysis based statistical similarity features from the measurements, and selecting uniformly distributed subsets to build representative reference sets. The utility of the proposed method is demonstrated by improvements in adaptive learning performance for the estimation of fatigue damage in an aluminum compact tension sample.
AB - Adaptive learning techniques have recently been considered for structural health monitoring applications due to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of environmental and operating conditions. In this paper, an active learning data selection procedure is proposed that adaptively selects the most informative measurements to include, from multiple available measurements, in estimating structural damage. This is important, since not all the measurements may provide useful information and could reduce performance when processed. Within the adaptive learning framework, the data selection problem is formulated to choose those measurements which are most representative of the diversity within a damage state. This is achieved by extracting time-frequency analysis based statistical similarity features from the measurements, and selecting uniformly distributed subsets to build representative reference sets. The utility of the proposed method is demonstrated by improvements in adaptive learning performance for the estimation of fatigue damage in an aluminum compact tension sample.
KW - active data selection
KW - adaptive learning
KW - damage classification
KW - discrepancy
KW - matching pursuit decomposition
KW - optimization
KW - statistical similarity
KW - structural health monitoring
KW - time-frequency analysis
UR - http://www.scopus.com/inward/record.url?scp=77953443630&partnerID=8YFLogxK
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U2 - 10.1117/12.848891
DO - 10.1117/12.848891
M3 - Conference contribution
AN - SCOPUS:77953443630
SN - 9780819480644
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010
T2 - Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2010
Y2 - 8 March 2010 through 11 March 2010
ER -