Transformer Health Index Prediction Using Static and Temporal Learning Models

Transformer health index (THI) prediction supports condition-based maintenance by mapping dissolved-gas, oil-quality, and furan indicators to an interpretable asset-condition score. This study develops a supervised benchmarking framework using 3,392 transformer oil diagnostic records from 510 transform-ers. Thirteen diagnostic features from dissolved gas analysis, oil quality analysis, and furan analysis are evaluated across three modelling groups: static machine learning, static deep learning, and variable-length temporal deep learning. Transformer-level splitting is applied to reduce data leakage from repeated trans-former histories. Static gradient-boosted tree models achieved the strongest results. XGBoost obtained the lowest test root mean square error of 3.4254 with a coefficient of determination of 0.9780 and health-index class accuracy of 88.20 percent. The best temporal model, the liquid time-constant neural network (LTC-LNN), achieved a test root mean square error of 4.8691 and a coefficient of determination of 0.9567. Permutation feature importance identified 2FAL, C2H2, and dielectric break-down as the most influential predictors. For the present dataset, static gradient-boosted tree models produced the strongest ob-served point-estimate performance, while LTC-LNN remained a promising temporal-learning alternative for repeated diagnostic histories. However, the uncertainty analysis indicates that small numerical differences among the leading static models should not be interpreted as definitive evidence of model superiority.