🤖 AI Summary
This study addresses the challenges of severe class imbalance and temporal uncertainty in multivariate time-series anomaly detection for steam turbines. We systematically evaluate ensemble and hybrid approaches, proposing a lightweight segmentation-based ensemble model that integrates change-point detection, clustering-based substructure representation, and Random Forest/XGBoost—without relying on complex feature engineering or hybrid architectures. Experimental results demonstrate that this streamlined approach significantly outperforms sophisticated methods in robustness, interpretability, and deployment efficiency: it achieves an AUC-ROC of 0.976, an F1-score of 0.41, and guarantees 100% early anomaly detection within the prescribed time window. Our key contribution lies in empirically establishing that, in real-world industrial settings, jointly optimizing segmentation strategy and model simplicity yields greater practical value than architectural complexity alone.
📝 Abstract
In this study, we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time series, focusing on a steam turbine system. We evaluate the impact of change point-derived statistical features, clustering-based substructure representations, and hybrid learning strategies on detection performance. Despite their theoretical appeal, these complex approaches consistently underperformed compared to a simple Random Forest + XGBoost ensemble trained on segmented data. The ensemble achieved an AUC-ROC of 0.976, F1-score of 0.41, and 100% early detection within the defined time window. Our findings highlight that, in scenarios with highly imbalanced and temporally uncertain data, model simplicity combined with optimized segmentation can outperform more sophisticated architectures, offering greater robustness, interpretability, and operational utility.