  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.
