Abstract
To solve the difficulties in extracting general features of few-shot high-dimensional structural health monitoring data and making accurate repair decision, a civil aircraft structural repair decision method based on deep meta-learning (DML) is proposed in this paper. Firstly, multi-source structural health monitoring data such as component name, cumulative flight hours, inspection level and state parameters are collected as model input. In addition, the damage condition, repair level and repair definition are used as data labels. Secondly, DML model is used for few-shot learning of repair cases. Monitoring data and corresponding labels make up different target datasets, which can be used for training of base-learner and meta-learner. Thirdly, the historical cases in manual are used for in-sample prediction by meta-learning model, while the new cases are utilized for out-of-sample prediction by fine-tuning the initialized deep meta-learning model. Finally, the aircraft aluminum lap joint damage dataset is used for case study. The results show that the proposed deep meta-learning model can achieve higher accuracy when dealing with few-shot high-dimensional input. Compared with other models, the task oriented DML model has more strong generalization ability and higher classification accuracy in analyzing multi-task cases.
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