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 Rengasamy, Priyadharshini; R, Rajesh Title A name given to the resource Explainable artificial intelligence framework for wind turbine fault detection using random forest Extreme gradient boosting hybrid model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Results in Engineering;Volume;28;Issue;;Article No.;107446; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.rineng.2025.107446" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.rineng.2025.107446</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017424887?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017424887?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 Rengasamy P., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram Post, Karnataka, Bangalore, 560029, India; R R., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram Post, Karnataka, Bangalore, 560029, India Description An account of the resource Though wind energy has great promise for clean energy generation in India, operational inefficiencies and underutilization still present major obstacles. Although installed wind capacity exceeds 51. 3 GW, actual power generation is still significantly lower than predicted mostly because of weak fault detection and maintenance techniques. Existing machine learning (ML) methods offer high accuracy but typically lack transparency in their forecasts, therefore making it difficult for engineers to correctly interpret and act on model outputs. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. A hybrid ensemble approach integrating Random Forest and XGBoost is proposed, combined with Local Interpretable Model-Agnostic Explanations (LIME) to provide local interpretability of predictions. The model was trained and evaluated on actual SCADA data using SelectKBest for feature selection, SMOTE for handling class imbalance, and RandomizedSearchCV for hyperparameter optimization. The tuned hybrid model achieved outstanding performance, with an accuracy of 0.9995, F1-score of 0.9995, and minimal error rates (MAE and MSE = 0.00052). LIME-based interpretability highlighted key features driving predictions, with Nacelle Temperature and Gearbox Temperature consistently emerging as critical indicators of turbine braking events, underscoring the importance of thermal variables in fault diagnosis. The findings suggest that interpretable machine learning not only enhances root cause analysis but also supports proactive maintenance, particularly by emphasizing improvements to cooling systems to reduce thermal failures. By providing transparent and reliable insights, the proposed solution enables wind farm operators to make informed, timely decisions, thereby improving turbine reliability and energy yield. The framework is practical, explainable, and well-suited for deployment in smart wind farms, aligning with the United Nations Sustainable Development Goals, including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production) 2025 The Author(s) Subject The topic of the resource Anomaly detection; Explainable AI(XAI); LIME (local interpretable model-agnostic explanations); Machine learning; Predictive maintenance; Random forest; SCADA data; SDG 12; SDG 7; SDG 9; SMOTE; Sustainable energy; Wind turbine generator Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 25901230; 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 All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online