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An Outlier Analysis Framework for Impedance-based文档
LA-UR-03-5396, Journal of Sound and Vibration, 286(1), 229-250, 2005
An Outlier Analysis Framework for Impedance-based
Structural Health Monitoring
*
Gyuhae Park , Amanda C. Rutherford, Hoon Sohn, Charles R. Farrar
Engineering Sciences Applications Division
Los Alamos National Laboratory
Los Alamos, NM 87545
ABSTRACT
This paper presents the use of statistically rigorous algorithms combined with
active-sensing impedance methods for damage identification in engineering systems. In
particular, we propose to use statistical pattern recognition methods to address damage
classification and data mining issues associated with the examination of large numbers of
impedance signals for health monitoring applications.
In this paper, in order to diagnose damage with statistical confidence, the
impedance-based monitoring is cast in the context of an outlier detection framework. A
modified auto-regressive model with exogenous inputs (ARX) in the frequency domain is
developed and the damage sensitive feature is obtained by quantifying the differences
between the measured impedance and the output of the ARX model. Furthermore,
because of the non-Gaussian nature of the feature distribution tails, extreme value
statistics (EVS) is employed to develop a robust damage classifier. This paper concludes
with a numerical example on a 5 degree-of-freedom system and an experimental
investigation on a multi-story building model to demonstrate the performance of the
proposed concept.
*
Author to whom corresponde
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