Abstract

A supervised machine learning framework is proposed for local assessments of delamination and transducer debonding in smart composite laminates while using their low-frequency structural vibrations. Load independent discriminative features were identified through a system identification algorithm and several supervised machine learning algorithms were employed to distinguish between the healthy and damaged structures. Linear discriminant analysis was shown to outperform other classifiers. The issue of overfitting of the training data was addressed by evaluating the predictive performance of the classifier on independent test cases. The proposed approach could help provide insightful guidelines for the assessment of multidamages in smart composite laminates.

摘要

提出了一种监督机器学习框架，用于在使用其低频结构振动时对智能复合层压板中的分层和换能器脱粘进行局部评估。通过系统识别算法识别与负载无关的判别特征，并采用几种监督机器学习算法来区分健康和受损结构。线性判别分析显示优于其他分类器。通过评估分类器在独立测试用例上的预测性能来解决训练数据的过度拟合问题。所提出的方法有助于为评估智能复合材料层压板的多重损伤提供有见地的指导方针。