Harnessing deep learning for hierarchical sensor anomaly detection in structure health monitoring of pressure vessel

Q. Zhou; Y. Zhang; J. Tang

doi:10.1117/12.3011034

9 May 2024 Harnessing deep learning for hierarchical sensor anomaly detection in structure health monitoring of pressure vessel

Q. Zhou, Y. Zhang, J. Tang

Author Affiliations +

Proceedings Volume 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024; 129490U (2024) https://doi.org/10.1117/12.3011034
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Q. Zhou, Y. Zhang, and J. Tang "Harnessing deep learning for hierarchical sensor anomaly detection in structure health monitoring of pressure vessel", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129490U (9 May 2024); https://doi.org/10.1117/12.3011034

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