Real-time sensing digital twin for aerospace structural health monitoring
File(s)
Published version
Author(s)
Di Fiore, Francesco
Ariyaratnam, Shapeetha
Ermacora, Mirko
Mainini, Laura
Type
Journal Article
Abstract
Modern aerospace systems depend on dense sensor networks that continuously interact with Structural Health Monitoring (SHM) systems to support predictive maintenance and flight safety. However, the reliability of sensor data is often taken for granted, and existing SHM frameworks primarily focus on detecting structural damage without assessing the health of the sensing layer itself. This limitation can lead to ambiguous interpretations, where structural degradations and sensor faults produce similar anomalies in the measured responses, resulting in false alarms or missed detections. To overcome this challenge, this paper introduces a sensing digital twin that extends the concept of structural digitalization to the sensing infrastructure. The proposed framework establishes a physics-informed, self-consistent virtual twin of the sensing network designed to monitor, correct, and quantify the reliability of measurements in real time. The twin is assembled by integrating reduced-order models of the structural dynamics, optimal sensor placement, and realistic sensor-fault mechanisms into an augmented temporal dataset. Three core modules are trained and embedded within the twin: a physics-aware classifier that discriminates between structural and sensing degradations, a correction module that reconstructs signals affected by sensors faults through manifold projections, and a reliability module that estimates the probabilistic level of trust associated with each sensor. The capabilities of the sensing digital twin are demonstrated on a composite wing panel undergoing progressive damage and sensor degradations. Results show that the framework achieves in real-time high classification accuracy, effective signal correction, and complete recovery of sensing reliability, supporting physically coherent and reliability-aware data for SHM under representative aerospace operating conditions.
Date Issued
2026-06-08
Date Acceptance
2026-03-26
Citation
Structural and Multidisciplinary Optimization, 2026, 69 (6)
ISSN
1615-147X
Publisher
Springer Science and Business Media LLC
Journal / Book Title
Structural and Multidisciplinary Optimization
Volume
69
Issue
6
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
159
Date Publish Online
2026-06-08