Misalignment characteristic analysis based on Kernel principal component analysis

Huimin Li, Xiaojian Ma, Yanbing Wang, Lawrence Bergman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new method based kernel principal component analysis (KPCA) is used to extract interesting misalignment features from a dynamical system. In this method, the projections (PCs) of the image of a test point with misalignment onto the nonlinear principal components in normal condition in featured space F are computed to represent the misalignment characteristics. It is shown in this work that the exploitation of the projections combination can improve the detection results. Even the varying trends of misalignment fault could be identified by use of this detection method. The method is illustrated on an experimental example of an auxiliary magnetic bearing rotor system.

Original languageEnglish (US)
Title of host publicationProceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011
Pages293-296
Number of pages4
DOIs
StatePublished - Aug 29 2011
Event4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 - Kunming, Lijiang, Yunnan, China
Duration: Apr 15 2011Apr 19 2011

Publication series

NameProceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011

Other

Other4th International Joint Conference on Computational Sciences and Optimization, CSO 2011
CountryChina
CityKunming, Lijiang, Yunnan
Period4/15/114/19/11

Keywords

  • Angular misalignment
  • Fault diagnose
  • Kernel PCA

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Optimization

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  • Cite this

    Li, H., Ma, X., Wang, Y., & Bergman, L. (2011). Misalignment characteristic analysis based on Kernel principal component analysis. In Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 (pp. 293-296). [5957664] (Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011). https://doi.org/10.1109/CSO.2011.167