In the realm of structure health monitoring for pressure vessels intended for space habitats, identifying sensor anomalies is of critical importance. The sensor anomalies are data patterns that diverge from anticipated measurement behaviors. To address the multifaceted challenges, we propose a hierarchical mechanism for sensor anomaly detection. This strategic approach not only filters out aberrant data but also subsequently ensures the extraction of reliable results for structure health monitoring, providing a safeguard against potential erroneous decision-making. Furthermore, this approach allows for efficient data handling across multiple sensors and incorporates physical knowledge into the deep learning model to comprehensively detect any sensor anomalies that are physically implausible. As a result, we achieve a more holistic and robust detection of sensor anomalies, ensuring heightened reliability in health monitoring for pressure vessel.
|