Applicability of linear and nonlinear principal component analysis for damage detection

The goal of this work is to detect structural damage using vibration-based damage identification approaches even when the damage-sensitive features are camouflaged by the presence of operational and environmental conditions. For feature classification purposes, four machine learning algorithms are applied based on the principal component analysis (PCA), nonlinear PCA, kernel PCA and greedy kernel PCA. Time-series data from an array of accelerometers under several structural state conditions were obtained from a well-known base-excited three-story frame structure. The main contribution of this work is the applicability of those PCA-based algorithms, for damage detection, in the presence of operational and environmental effects. For these specific data sets, one can infer that the greedy kernel PCA algorithm is more appropriate when one wants to minimize false-positive indications of damage without increasing, significantly, the false-negative indications of damage.