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Principal Component Analysis (PCA) as a Statistical Tool for Identifying Key Indicators of Nuclear Power Plant Cable Insulation Degradation

  • Chamila C. De SilvaEmail author
  • Scott P. Beckman
  • Shuaishuai Liu
  • Nicola Bowler
Conference paper
Part of the The Minerals, Metals & Materials Series book series (MMMS)

Abstract

This paper describes the use of Principal Component Analysis (PCA) as a statistical method to identify key indicators of degradation in nuclear power plant cable insulation. Seven kinds of single-point data and four kinds of spectral data were measured on cross-linked polyethylene (XLPE) that had undergone aging at various doses and dose rates of gamma radiation from a cobalt 60 source, and various elevated temperatures. To find the key indicators of degradation of aged cable insulation, PCA was used to reduce the dimensionality of the data set while retaining the variation present in the original data set. For example, PCA reveals that, for material aged at 90 °C, elastic modulus shows a positive correlation with total dose while mass loss, oxidation induction time and density show negative correlations with the same parameter.

Keywords

Aged XLPE Characterization methods Principal component analysis 

Notes

Acknowledgements

This work was funded by the DOE Office of Nuclear Energy’s Nuclear Energy University Programs under contract number DENE0008269 and the DOE Office of Nuclear Energy’s Light Water Reactor Sustainability Program. Exposure experiments were conducted at Pacific Northwest National Laboratory, which is operated by Battelle for the US DOE under contract DE-AC05-76RL01830.

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Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  • Chamila C. De Silva
    • 1
    Email author
  • Scott P. Beckman
    • 2
  • Shuaishuai Liu
    • 1
  • Nicola Bowler
    • 1
  1. 1.Iowa State UniversityAmesUSA
  2. 2.Washington State UniversityPullmanUSA

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