一种集成多个机器学习模型的复合材料结构损伤识别方法

Integrated Method of Multiple Machine-Learning Models for Damage Recognition of Composite Structures

针对基于导波的复合材料结构损伤监测手段在实际工程应用中遇到的问题,结合目前已开展的利用机器学习模型辅助结构损伤识别的经验,提出了一种基于最小边际系数的复合材料结构损伤识别方法。通过采用多个机器学习模型对监测数据进行预测,利用不同机器学习模型之间的差异性和在不同数据分布上的预测置信度,提高整体结构损伤识别的泛化能力。通过试验验证,该方法能明显提高基于导波的复合材料结构损伤识别精度。

In the topic of damage detection of composite structures based on lamb wave technology,damage index is commonly used for damage identification.However,its threshold is largely of expertisedependence and poor performance at knowledge generalization.Therefore,a method based on the concept of least margin is proposed,which integrates even machine learning models and outputs the identification result by polling all models’decision.The proposed method avoids the shortage that damage recognition relies on a single but incomprehensive model,and puts the confidence on a number of most qualified models instead.Significantly higher accuracy of damage identification for composite structures is manifested through test verification.