Nonlinear System Identification for Damage Detection.

摘要

This report has been developed based on information exchanges at a two-day workshop on nonlinear system identification for damage detection that was held July 25–26, 2006, at Los Alamos National Laboratory. The workshop is the second in a series that was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. The Institute’s research and education focus is to promote and further develop the multidisciplinary fields of structural health monitoring (SHM), damage prognosis and model validation and uncertainty quantification. The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as SHM. A statistical pattern recognition paradigm for SHM is first presented and the concept of nonlinear system identification is addressed with respect to the feature extraction portion of this paradigm. In many cases damage causes a structure that initially behaves in a predominantly linear manner to exhibit nonlinear response when subject to its operating environment. The formation of cracks that subsequently open and close under operating loads is an example of such damage. The damage detection process can be significantly enhanced if one takes advantage of these nonlinear effects when extracting damage-sensitive features from measured data. This report will provide examples from nonlinear dynamical systems theory and from nonlinear system identification techniques that are used for the feature extraction portion of the damage detection process. The report concludes by defining some future research needs and directions that are aimed at transitioning the concept of nonlinear system identification for damage detection from laboratory research to field-deployed engineering systems