AI-Based Data Fusion of IEC Methods with Dashboard Analytics for Hydrophobicity Evaluation of High-Voltage Insulators

Reliable assessment of insulator surface hydrophobicity is critical for the safety and continuity of power transmission and distribution networks and electrified transportation. While IEC TS 62073 prescribes three established methods—Contact angle(CA), Surface tension(SF), and Spray method(SP) —their deployment is hampered by operator subjectivity, environmental sensitivity, and the lack of an integrated interpretation. We present an AI-assisted, IEC-faithful framework that unifies contact angle, surface tension, and spray method into a single decision pipeline and exposes actionable outputs via a maintenance-oriented dashboard. Methodologically, Contact Angle is automated using Linear Regression Intersection for robust left/right contact estimation; Surface Tension is automated via cotton-tip last-frame and stationery-frame detection to time droplet reformation without manual frame picking; and Spray method leverages Zoning Feature Extraction with k-nearest neighbors, optionally complemented by an SVD–Mahalanobis classifier for borderline cases. Experiments on porcelain type 52-1 insulators under control conditions (30 °C, RH > 70%) comprise 90 droplet sequences for Contact angle, Surface tension and 2,800 Spray method images. As a result, applying these algorithms across the three methods yields interpretation accuracy exceeding 90%. Decision-level fusion improves consistency in intermediate classes while preserving IEC interpretability, and the dashboard provides transparent confidence and trend views for field engineers. In conclusion, the proposed algorithms increase the accuracy and reliability of insulator test interpretation and reduce ambiguity among visually similar features, enabling standards-compliant, reproducible assessments suitable for predictive-maintenance workflows in utilities and rail operations.