Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandramohan, J.; Karthick, K.; K, Aruna S; Ponkumar, G. Title A name given to the resource A robust explainable machine learning pipeline for transformer health index prediction addressing data pathologies and redundancy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Electric Power Systems Research;Volume;259;Issue;;Article No.;113275; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.epsr.2026.113275" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.epsr.2026.113275</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038222544?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038222544?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandramohan J., Department of Electrical and Electronics Engineering, Gnanamani College of Technology, Pachal, Namakkal, 637018, India; Karthick K., Department of Electrical and Electronics Engineering, GMR Institute of Technology (GMRIT) (Deemed to be University), Andhra Pradesh, Rajam, 532127, India; K A.S., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, 560074, India; Ponkumar G., Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, 600123, India Description An account of the resource Power transformers are critical infrastructure assets where unexpected failures incur severe technical and economic penalties. This study proposes a robust, explainable machine-learning (ML) pipeline for predicting the transformer Health Index (HI) using routinely collected dissolved gas analysis (DGA) and dielectric measurements. To ensure model reliability, the pipeline specifically addresses data pathologiesnamely extreme skewness and heavy tailsusing YeoJohnson transformations, while mitigating multicollinearity through hierarchical correlation clustering (|r| ? 0.85) followed by a Variance Inflation Factor (VIF) screening (VIF ? 5). Four high-performance ensemblesRandom Forest, XGBoost, LightGBM, and CatBoostwere optimized via randomized cross-validation. Experimental results on a dataset of 470 records demonstrate consistent generalization across all models (RMSE ? 0.022), with Random Forest providing superior accuracy (MAPE ? 1.24%). A Taylor diagram confirmed consistent generalization (correlation ? 0.730.78 and matched variance), while residual analysis showed minimal bias. SHAP explanations indicated that dibenzyl disulfide (DBDS) and interfacial tension (Interfacial V) were the most influential positive drivers of HI; water content tended to depress HI; and several gases (e.g., methane, hydrogen, acetylene, CO) contributed positively at higher concentrations. The proposed workflow was robust to skew/heavy tails and multicollinearity, required no feature scaling, and produced transparent, practitioner-ready insights that support condition-based maintenance at fleet scale. 2026 Elsevier B.V. Subject The topic of the resource Dissolved gas analysis; Health index prediction; Machine learning; Multicollinearity; Power transformer Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 3787796; CODEN: EPSRD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online