Non-Destructive Assessment of Gamma Radiation Aging in Nuclear Cables via New Dielectric Spectroscopy Markers and Machine Learning Algorithm
Ahmad Abualasal, Zoltán Ádám Tamus

TL;DR
This paper introduces a non-destructive method to assess radiation damage in nuclear cables using dielectric spectroscopy and machine learning.
Contribution
The study introduces novel dielectric markers and a machine learning framework for non-destructive radiation aging assessment in nuclear cables.
Findings
Central capacitance (CC) and (C × F × LF) show high sensitivity to radiation in EPR insulation materials.
CSPE jackets exhibit a monotonic decrease in central frequency (CF) with increasing radiation exposure.
The method enables accurate and transparent diagnosis of irradiated cables without destructive testing.
Abstract
Low-voltage instrumentation and control (I&C) cables in nuclear power plants are continuously exposed to gamma (γ) radiation within containment areas, leading to cumulative degradation of their polymer insulation over decades of operation. Since conventional mechanical aging assessments are destructive, this study establishes a non-destructive diagnostic framework using high-frequency dielectric spectroscopy. Cable samples with ethylene propylene rubber (EPR) insulation and chlorosulfonated polyethylene (CSPE) jackets were subjected to controlled γ-irradiation at doses up to 1200 kGy. The broadband dielectric response was analyzed along with derived novel diagnostic parameters from capacitance and loss tangent spectra and a machine learning AI algorithm. The results show a strong, material-dependent relationship between radiation dose and dielectric indicators. For EPR insulation, the…
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Taxonomy
TopicsHigh voltage insulation and dielectric phenomena · Power Transformer Diagnostics and Insulation · Polymer Science and PVC
