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A pattern recognition and data analysis method for maintenance management

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  • Fausto Márquez
  • Jesús Muñoz

Abstract

This article presents a pattern recognition method based on grouping by linear relationship a set of faults. The majority of faults can be detected, but only a few experiments can be identified. The algorithm called Principal Component Analysis (PCA) is employed together with the statistical parameters of the signals for detecting and identifying the faults. PCA technique is utilised for modifying dataset reducing the coordinate system, which must be correlated, by linear transformation, into a smaller set of uncorrelated variables called ‘principal components’. The signals analysed were the current and force signals in normal-to-reverse and reverse-to-normal directions of the system.

Suggested Citation

  • Fausto Márquez & Jesús Muñoz, 2012. "A pattern recognition and data analysis method for maintenance management," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(6), pages 1014-1028.
    • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:6:p:1014-1028
    DOI: 10.1080/00207720903045809
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