Anomaly detection and model interpretation are key components for robots deployed in safety-critical scenarios. In this paper, we propose to use Sentinel —a Transformer-based architecture for multivariate time series forecasting —to improve anomaly detection performance for mobile robots, and we investigate whether the model’s attention mechanisms faithfully reflect the underlying statistical structure of the data. Our results on the ALFA dataset (a widely used aerospace benchmark) demonstrate that Sentinel achieves good anomaly detection performance when compared to state-of-the-art approaches. Moreover, the empirical evaluation shows that sentinel's attention mechanisms capture relevant dependencies among the features hence offering key insight for early warning indicators. These findings highlight the potential of attention-based interpretability in complex, sensor-rich robotic environments and pave the way towards explainable and resilient anomaly detection frameworks.