A transfer learning approach for data-driven localization of damage areas in plate-like structures of CFRP materials

Abstract

The application of machine learning techniques based on Lamb waves for structural health monitoring of composite material damage has been widely adopted. However, this method often struggles to acquire sufficient labeled historical Lamb wave signals for training machine learning models. This issue can be addressed through the implementation of transfer learning. This paper proposes a data-driven transfer learning model for locating coordinates of damage areas in plate-like structures with complex geometric features such as rivets and grooves. Based on the differences in conditional distributions in various structural health monitoring scenarios, a Cross-Component Regression for Damage Localization Model (CRDM) is introduced for damage localization across components. Due to the limited availability of labeled target data, CRDM aims to preserve global properties of the target data-dominated conditional distribution, besides considering the predictive accuracy of individual samples. Therefore, a hybrid loss function is constructed by combining Mean Squared Error (MSE) and Conditional Embedding Operator Difference (CEOD). Subsequently, the target model is fine-tuned based on this loss function. Experimental validations are conducted on finite element models and real datasets, confirming the effectiveness of the CRDM model in damage localization for plate-like structures.