点击次数：
影响因子：2.8
DOI码：10.2514/1.J064808
发表刊物：AIAA Journal
摘要：Quantifying structural damage using online monitoring data is crucial for condition-based maintenance to ensure aviation safety. However, most data-driven methods hardly use accumulated domain knowledge, making it difficult to address parameter variability across different structures due to manufacturing as well as compromising result interpretability. To address these challenges, this study proposes a physics-decoded variational neural network for structural damage quantification and model parameter calibration. The innovation of this method lies in seamlessly integrating a reduced-order digital twin containing damage states and influencing parameters as a decoder within the variational neural network and training a data-driven physical feature extraction model using the variational inference. This architecture enables the individualized, real-time structural damage quantification and parameter calibration across an entire fleet, while accounting for uncertainties. Validation on typical damaged aeronautical panels demonstrates that the proposed method accurately predicts structural damage states and quantifies associated uncertainties, thereby ensuring high interpretability and accuracy. This approach is expected to be integrated into the airframe digital twin framework to enable condition-based maintenance across a fleet.
合写作者：周轩*,Marco Giglio,Claudio Sbarufatti,董雷霆*
第一作者：许家祺
论文类型：期刊论文
一级学科：航空宇航科学与技术
文献类型：期刊
卷号：63
期号：6
页面范围：2474-2486
是否译文：否
发表时间：2025-06-01
收录刊物：SCI
发布期刊链接：https://arc.aiaa.org/doi/10.2514/1.J064808