Online damage quantification suffers from insufficient labeled data. In this context, adopting the domain adaptation on historical labeled data from similar structures/damages 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 conditional distribution discrepancy is measured, and domain adaptation can simultaneously consider the marginal and conditional distribution for the regression task. Furthermore, a framework of online damage quantification integrated with the proposed domain adaptation method is presented. The method has been verified with an example of a damaged helicopter panel, in which domain adaptations are conducted across different damage locations and from simulation to experiment, proving the accuracy of damage quantification can be improved significantly even in a noisy environment. It is expected that the proposed approach to be applied to the fleet-level digital twin considering the individual differences.
翻译:在线损害量化存在标签不足的数据,因此,对来自类似结构/损害的历史标签数据进行域性调整,以协助目前的诊断任务,这样做是有益的;然而,大多数域性调整方法都是为分类设计的,无法有效地处理损害量化,这是一个持续实际估价标签的回归问题。本研究首先提出一种新的域适应方法,即基于在线模糊设置的回归联合分配适应,以应对这一挑战。通过将连续真实价值标签转换为通过模糊装置的模糊等级标签,对有条件分布差异进行测量,域调整可以同时考虑回归任务的边际和有条件分布。此外,还提出了一个与拟议的域适应方法相结合的在线损害量化框架。该方法经过一个损坏的直升机专门小组的验证,该专门小组在不同损害地点进行领域调整,从模拟到试验,证明损害量化的准确性即使在噪音环境中也能大大提高。预计,考虑到个别差异,拟对舰队级数字组合采用的拟议方法。