Abstract
This research aims at revealing the rules that the impact of kernel function and its parameters on the performance of kernel principle component analysis (KPCA) for dimensionality reduction. KPCA was performed on nine databases by using different kernel functions and a series of equal space kernel parameters. The relation charters between kernel parameters and the number of kernel principle components were constituted. It found that the Gussian kernel and its parameter above 25 are the best choice for rotating machinery feature vector dimensionality reduction by using KPCA. This study presents a reference and gist for the application of KPCA in rotating machinery fault diagnostic case.
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