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[Submitted on 15 Oct 2026] Title:Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft View a PDF of the paper titled Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft, by Alvaro Peiro and 1 other authors View PDF Abstract:The signal generation capabilities of a novel data augmentation framework are examined in aerospace structures. First, the framework learns class-conditional temporal and spectral characteristics of the measured structural responses. It uses conditional generative adversarial network training and Monte Carlo dropout uncertainty quantification as a Bayesian approximation. This step enables stochastic signal synthesis and uncertainty quantification. Second, the generated responses are rigorously compared against experimental measurements across time, frequency, uncertainty, variability, and feature domains to assess physical realism, statistical consistency, and distributional fidelity. Finally, the synthetic data are evaluated to determine whether they should be incorporated into a supervised damage classification workflow. The framework is validated on the full-scale benchmark dataset from a BAE Systems Hawk T1A aircraft tested at the University of Sheffield's Structural Dynamics Laboratory for Verification and Validation. The test involves acceleration, fibre Bragg grating strain, and nonlinear dynamic response measurements. The results demonstrate that the model accurately captures dominant structural dynamics, it guides on signal use for classification improvement based on uncertainty characteristics, and it provides meaningful uncertainty estimates linked to signal complexity and excitation characteristics. Importantly, the framework offers a realistic generative modelling plan as a robust digital twin component for data-driven structural health monitoring, supporting resilient decision-making for condition assessment in safety-critical aerospace systems. Comments: 18 pages; 18 figures, published paper Subjects: Machine Learning (cs.LG); Signal Processing (eess.SP) MSC classes: 68T07, 68T09, 68T37, 62P30, 62M45, 74Q05, 74H50 ACM classes: I.2.6; I.5.1; I.5.2; I.5.4; G.3 Cite as: arXiv:2610.22436 [cs.LG] (or arXiv:2610.22436v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.22436 arXiv-issued DOI via DataCite Journal reference: Alvaro Peiro, Marios Impraimakis, Aerospace Science and Technology, Volume 180, Part 5, 2027, Article 114028 Related DOI: https://doi.org/10.1016/j.ast.2026.114028 DOI(s) linking to related resources Submission history From: Marios Impraimakis [view email] [v1] Thu, 15 Oct 2026 11:32:37 UTC (11,954 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft, by Alvaro Peiro and 1 other authors View PDF view license Additional Features Audio Summary Current browse context: cs.LG new | recent | 2026-10 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar Loading... 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