Damage identification using multivariate statistics: Kernel discriminant analysis

This paper is part of a series which illustrates how modern methods of mult ivariate statistics can be used to solve, or illuminate, damage identificat ion 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 train ed on the data after an initial pre-processing stage and also after a furth er statistical dimension reduction. The results from KDA are compared with a benchmark statistical method and with a neural network classifier.