Predicting Micro-Crack Formation in XLPE Main Cables of Educational Building Power Systems Using Real-Time Three-Phase Current Data Analysis
DOI: 10.14416/j.ind.tech.2026.04.009
Keywords:
micro-crack prediction, XLPE cable, load unbalance, real-time analysis, predictive maintenanceAbstract
This study presents a predictive approach for detecting micro-crack formation in XLPE main cables of an educational building power system using real-time three-phase current data collected over seven months (>60,000 samples) via an IoT monitoring system. After data cleaning and 5-minute resampling, statistical and temporal analyses revealed that Phase B consistently carried the highest load (7.41 ± 2.28 A in January 2025), while the Load Unbalance Index (LUI) rose from 8.45% to 13.05%, reflecting moderate imbalance. Cumulative thermal energy (I²) increased by 104%, with a strong correlation to LUI (r = 0.89). Logistic Regression and LSTM models estimated the probability of micro-crack occurrence , identifying a high-risk period in early January 2025 corresponding to a +9.7% current surge. The results confirm that real-time current analysis effectively indicates early XLPE insulation degradation and supports predictive maintenance for improved system reliability and energy management.
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