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

机译：主成分分析（PCA）作为统计工具，用于识别核电站电缆绝缘退化​​的主要指标

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摘要

This thesis 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 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 at 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. The analysis revealed that, for material aged at both 60°C and at 90°C, oxidation induction time and elongation at break data have the greatest negative correlations with the total dose to which the sample has been exposed. Furthermore, multiple linear regression models were used to construct equations that predict the values of one dimension as a function of dose rate and total dose (number of days of exposure). In this data set, oxidation induction time was found to be the only dimension that was successfully predicted via multiple regression equations for XLPE samples aged at both 60°C and at 90°C.
机译：本文介绍了使用主成分分析（PCA）作为一种统计方法来识别核电厂电缆绝缘层退化的主要指标。在交联聚乙烯（XLPE）上测量了七种单点数据，该交联聚乙烯以各种剂量和来自60钴源的伽马射线的剂量率以及在各种高温下进行了老化。为了找到老化的电缆绝缘性能退化的关键指标，使用PCA来降低数据集的维数，同时保留原始数据集中存在的差异。分析表明，对于在60°C和90°C时效的材料，氧化诱导时间和断裂伸长率数据与样品所暴露的总剂量之间具有最大的负相关性。此外，使用多个线性回归模型构建方程，该方程预测一维值与剂量率和总剂量（暴露天数）的关系。在该数据集中，发现氧化诱导时间是唯一通过多个回归方程成功预测60°C和90°C时效的XLPE样品的尺寸。

著录项

  • 作者

    De Silva, Chamila C.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Materials science.;Statistics.;Nuclear engineering.
  • 学位 M.S.
  • 年度 2017
  • 页码 79 p.
  • 总页数 79
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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