Akademik Veri Yönetim Sistemi
ELECTRICA, cilt.26, sa.1, ss.1-10, 2026 (ESCI, TRDizin)
Surface tracking resistance and long-term reliability are critical performance parameters for polymeric insulation materials used in high-voltage electrical applications. In this study, the tracking behavior of epoxy/aluminum oxide (Al2O3) nanocomposites was experimentally, statistically, and data-driven investigated under high electrical stress conditions. Epoxy composites containing 0, 1, 3, and 5 wt.% Al2O3 nanoparticles were fabricated and tested according to the International Electrotechnical Commission(IEC) 60112 standard at applied voltage levels of 400 V, 500 V, and 600 V, with repeated experiments conducted to ensure statistical consistency. The results demonstrated that Al2O3 incorporation significantly influences tracking resistance in a concentration-and voltage-dependent manner, where the composite containing 1 wt.% Al2O3 exhibited the highest tracking resistance at moderate voltage levels (400–500 V), achieving higher drop-to-failure values (defined as the number of contaminant droplets required to cause sustained surface tracking for more than 2 seconds or material burn-through, in accordance with IEC 60112) than pure epoxy. However, increasing the filler content beyond 1 wt.% led to a reduction in tracking lifetime, particularly under high electrical stress, which was associated with increased surface erosion and localized degradation. The degradation behavior at 600 V was further analyzed using two-parameter Weibull statistics, revealing a decrease in characteristic life with increasing Al2O3 content, while the 1 wt.% Al2O3 composite exhibited the highest Weibull shape parameter, indicating a stable and predictable wear-out dominated failure mechanism. In addition, machine learning models were developed to predict tracking lifetime with high accuracy (R2 > 98%), and feature importance analysis identified erosion-related mass loss, filler content, and applied voltage as the dominant parameters governing tracking degradation. Overall, the strong agreement between experimental results, Weibull reliability analysis, and machine learning predictions provides a combined experimental and data-driven framework for evaluating and optimizing epoxy-based insulation materials for high-voltage applications.