TY - GEN
T1 - Adaptive learning for damage classification in structural health monitoring
AU - Chakraborty, D.
AU - Kovvali, N.
AU - Zhang, J. J.
AU - Papandreou-Suppappola, Antonia
AU - Chattopadhyay, Aditi
PY - 2009/12/1
Y1 - 2009/12/1
N2 - A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology where stochastic models continuously evolve with the time-varying environment and Dirichlet process mixture models are utilized to self-adapt to structure within the data. Coupled with appropriate physics-based phenomenology, the approach provides an adaptive and effective framework for online SHM. The proposed technique is demonstrated for the detection of progressive fatigue damage in a metallic structure under variable-amplitude loading.
AB - A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology where stochastic models continuously evolve with the time-varying environment and Dirichlet process mixture models are utilized to self-adapt to structure within the data. Coupled with appropriate physics-based phenomenology, the approach provides an adaptive and effective framework for online SHM. The proposed technique is demonstrated for the detection of progressive fatigue damage in a metallic structure under variable-amplitude loading.
UR - http://www.scopus.com/inward/record.url?scp=77953865222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953865222&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2009.5469782
DO - 10.1109/ACSSC.2009.5469782
M3 - Conference contribution
AN - SCOPUS:77953865222
SN - 9781424458271
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1678
EP - 1682
BT - Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers
T2 - 43rd Asilomar Conference on Signals, Systems and Computers
Y2 - 1 November 2009 through 4 November 2009
ER -