A Self-Improving Digital Twin Framework with Drift Detection and Transparency

Digital twins operating in real-world cyber–physical systems must adapt to evolving environments where concept drift can degrade predictive performance. This paper proposes an adaptive digital twin framework that integrates online concept drift detection, validated model adaptation, and explainable decision support. A streaming monitoring pipeline continuously evaluates model behavior using error-based statistical indicators and triggers adaptive retraining when significant drift is detected. The framework incorporates interpretability mechanisms to justify adaptation decisions and ensure trustworthiness. Experimental evaluation on a controlled cyber–physical dataset with simulated drift demonstrates that the proposed approach reduces prediction error, recovery time, and cumulative error compared to static and threshold-based baselines. These results highlight the effectiveness of adaptive digital twins for maintaining reliability in non-stationary monitoring environments.