Few-shot meta transfer learning-based damage detection of composite structures

超参数 学习迁移 计算机科学 人工智能 领域(数学分析) 结构健康监测 机器学习 一次性 稀缺 复合数 任务(项目管理) 利用 结构工程 工程类 算法 系统工程 机械工程 数学 数学分析 计算机安全 经济 微观经济学
作者
Y.S. Chen,Xuebing Xu,Cheng Liu
出处
期刊:Smart Materials and Structures [IOP Publishing]
卷期号:33 (2): 025027-025027 被引量:2
标识
DOI:10.1088/1361-665x/ad1ded
摘要

Abstract Damage detection and localization using data-driven approaches in carbon fiber reinforced plastics (CFRP) composite structures is becoming increasingly important. However, the performance of conventional data-driven methods degrades greatly under little amount of data. In addition, the scarcity of data corresponding to defect/damage conditions of CFRP structures lead to extreme data imbalance, which make this problem even more challenging. To address these challenges of few training data and the scarcity of damage samples, this paper proposes a few-shot meta transfer learning (FMTL)-based approach for damage detection in CFRP composite structures. This method leverages knowledge learnt from an unbalanced data domain generated from a single CFRP composite sample and adapts the knowledge to be applied for other data domains generated by CFRP samples with different structural properties. The contributions of this research include demonstrating the feasibility of harnessing knowledge from notably limited experiment data, designing an algorithm for configuring hyperparameters based on a specific FMTL task, and identifying the impacts of hyperparameters on learning performances. Results show that FMTL can improve the recall rate by at least 15% while preserving the ability to identify health conditions. This method can be extremely useful when we need to monitor health condition of critical CFRP structures, like airplanes, because they can rarely generate data under damage conditions for model training. FMTL enables us to build new models based on unbalanced source domain data with the cost of a minimal set of samples from the target domain.
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