An Automated Non-Parametric Healthy Subspace Method for Unsupervised Robust Vibration-Based Damage Detection Under Uncertainty

摘要

An automated healthy subspace method for unsupervised robust vibration-based damage detection under uncertainty is postulated. The key idea lies with the approximation of the "healthy subspace" as the union of a number of hyper-spheres, with properly determined centers and radii. The method features full automation capability, aiming at eliminating user intervention, and very good detection performance. Its effectiveness is demonstrated via damage detection for a population of composite beams and comparisons with a Multiple Model based and a Random Coefficient Gaussian Mixture model based method.