Using attractor localization to improve nonlinear prediction error for structural health monitoring

摘要

Recently, damage sensitive features extracted from the phase space reconstruction of a structural response have proven to be successful for use in the field of structural health monitoring. One such feature utilizes the evolutions of randomly selected points on a baseline attractor to predict evolutions of corresponding points on an attractor in some unknown state of health. The error based on this prediction can be used to determine the presence and/or extent of damage. One drawback of this approach is that some regions of the attractor geometry may be more or less sensitive to damage-induced changes in the dynamics. Thus, prediction error could incur large variances in its distribution, and results could change significantly depending on the size and location of the randomly selected subset of points used for prediction. This paper examines the effect of spatial location on prediction error in an effort to better utilize the geometry of phase space. Investigations will involve a chaos-driven oscillator subject to parametric changes simulating damage.