Online damage quantification suffers from insufficient labeled data that weakens its accuracy. In this context, adopting the domain adaptation on historical labeled data from similar structures/damages or simulated digital twin data to assist the current diagnosis task would be beneficial. However, most domain adaptation methods are designed for classification and cannot efficiently address damage quantification, a regression problem with continuous real-valued labels. This study first proposes a novel domain adaptation method, the Online Fuzzy-set-based Joint Distribution Adaptation for Regression, to address this challenge. By converting the continuous real-valued labels to fuzzy class labels via fuzzy sets, the marginal and conditional distribution discrepancy are simultaneously measured to achieve the domain adaptation for the damage quantification task. Thanks to the superiority of the proposed method, a state-of-the-art online damage quantification framework based on domain adaptation is presented. Finally, the framework has been comprehensively demonstrated with a damaged helicopter panel, in which three types of damage domain adaptations (across different damage locations, across different damage types, and from simulation to experiment) are all conducted, proving the accuracy of damage quantification can be significantly improved in a realistic environment. It is expected that the proposed approach to be applied to the fleet-level digital twin considering the individual differences.

翻译：在线损伤量化因标记数据不足而影响其准确性。在此背景下，利用相似结构/损伤的历史标记数据或模拟数字孪生数据进行领域适配，以辅助当前的诊断任务将大有裨益。然而，大多数领域适配方法专为分类问题设计，难以有效处理具有连续实值标签的回归问题——损伤量化正是此类问题。本研究首次提出一种新颖的领域适配方法——基于模糊集的在线联合分布适配回归方法，以应对这一挑战。通过模糊集将连续实值标签转换为模糊类标签，同时度量边缘分布与条件分布的差异，从而实现损伤量化任务的领域适配。得益于所提方法的优越性，本文进一步提出了一种基于领域适配的先进在线损伤量化框架。最终，以受损直升机面板为对象对该框架进行全面验证，涵盖三种损伤领域适配场景（不同损伤位置、不同损伤类型、从仿真到实验），结果表明在真实环境中损伤量化精度可显著提升。预期该方法可应用于考虑个体差异的机队级数字孪生。