Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation

Date
2017-01-01
Authors
De Silva, Chamila
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Altmetrics
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Research Projects
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Abstract

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 à °C and at 90 à °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 à °C and at 90 à °C.

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Aged XLPE, Characterization methods, Multiple linear regression, Principal component Analysis
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