Damage identification using multivariate statistics: Kernel discriminant analysis

Abstract

This paper is part of a series which illustrates how modern methods of multivariate statistics can be used to solve, or illuminate, damage identification problems. The technique discussed here is Kernel Discriminant Analysis (KDA), which can be used to assign damage classifications to measured data vectors. The data discussed is experimental data from a ball bearing system in an undamaged state and in four damage states. The classifiers are trained on the data after an initial pre-processing stage and also after a further statistical dimension reduction. The results from KDA are compared with a benchmark statistical method and with a neural network classifier.