This paper introduces a novel approach for runtime validation and anomaly detection in Digital Twins. We enhance the trace alignment capabilities of the Needleman-Wunsch dynamic programming algorithm to enable continuous system state monitoring. Our method overcomes the limitations of the existing literature by eliminating the need for time series preprocessing and predefined behavioral constraints. By aligning traces and utilizing sliding windows, we periodically analyze the most recent snapshots to detect anomalies, delays, and deviations between the twins at runtime. This technique improves anomaly detection accuracy and system diagnostics by leveraging the behavioral duplication inherent in Digital Twins. We validated our prototype with elevator behavioral traces, demonstrating its effectiveness in measuring behavioral fidelity and monitoring system safety.