Temporal Attribution Matrix for Tracking XAI Feature Importance Evolution in Wind Turbine Gearbox Degradation Detection Using SCADA Data

Wind turbine gearbox condition monitoring increasingly combines Supervisory Control and Data Acquisition (SCADA) data with Explainable Artificial Intelligence (XAI) for predictive maintenance. However, current XAI applications report attributions as static or globally aggregated feature-importance results. Such representations do not reveal when fault-related variables emerge, how dominance shifts between features, or how the explanatory structure evolves as degradation progresses. This limits their value for time-resolved diagnostic interpretation. To address this gap, this study proposes the Temporal Attribution Matrix (TAM), a temporal interpretability framework that tracks the evolution of XAI-derived feature importance across degradation periods. The central hypothesis is that temporal attribution patterns contain diagnostic information not captured by static feature-importance summaries. TAM was applied to a three-year SCADA dataset from Fuhrländer FL2500 wind turbines using XGBoost-SHAP and 1D-CNN Grad-CAM within sliding weekly windows. Four temporal measures were derived: feature onset time, dominance transition, attribution entropy, and cross-model consistency. Both XAI methods independently identified gearbox bearing temperatures 451 and 152 as the most influential features. TAM further revealed a synchronized thermal-feature onset on 23 October 2012, 14 SHAP dominance transitions compared with 70 Grad-CAM transitions, and a moderate cross-model Spearman correlation of 0.488. Secondary validation using WT82 confirmed TAM’s applicability beyond a single turbine. These results demonstrate that TAM extends static XAI by producing time-resolved degradation narratives for SCADA-based wind turbine predictive maintenance.