Kernel PCA in nonlinear visualization of a healthy and a faulty planetary gearbox data
Anna M. Bartkowiak1, Radoslaw Zimroz2
1Wroclaw University, Institute of Computer Science, 50-383, Wroclaw, Poland, (retired)
2Wroclaw University of Technology,
Faculty of GeoEngineering Mining and Geology,
E-mail: firstname.lastname@example.org, email@example.com
Abstract. PCA (Principal Component Analysis) is a powerful method for investigating the dimensionality and extracting structure from multi-dimensional data, however it extracts only linear projections. More general projections – accounting for possible non-linearities among the observed variables – can be obtained using kPCA (Kernel PCA), that performs the same task, however working with an extended feature set. We consider planetary gearbox data given as two 15-dimensional data sets, one coming from a healthy and the other from a faulty planetary gearbox. For these data both the PCA (with 15 variables) and the kPCA (using indirectly 500 variables) is carried out. It appears that the investigated PC-s are to some extent similar; however, the first three kernel PC-s show the data structure with more details.
Keywords: visualization of MD data, principal components, PCA and kPCA, kernel trick.
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Cite this article
Bartkowiak Anna M., Zimroz Radoslaw Kernel PCA in nonlinear visualization of a healthy and a faulty planetary gearbox data. Vibroengineering PROCEDIA, Vol. 13, 2017, p. 62‑66.
© JVE International Ltd. Vibroengineering PROCEDIA. Sep 2017, Vol. 13. ISSN 2345-0533