A transfer learning approach for damage diagnosis in composite laminated plate using Lamb waves

Abstract

Abstract Lamb wave-based damage diagnosis systems are widely regarded as a likely candidate for real-time structural health monitoring (SHM), although analysing the Lamb wave response is still a challenging task due to its complex physics. Recently, deep learning (DL) models such as convolutional neural network (CNN) have shown robust classification performance in various structures using Lamb wave-based diagnostic strategies. However, these DL models are often designed to address isolated tasks, which means that the model needs to be re-trained from scratch to accommodate any small change to the setup. Thus, such data-dependency of the DL model designed for the SHM system can restrict its full usage. This paper presents a study on a version of the transfer learning framework (TLF) based on 1D-CNN autoencoder (AE) and a classifier as a possible way to address this problem. In the transfer learning approach, the knowledge learned by a network represented as source model, while performing one or more tasks is utilized to improve the damage diagnosing ability of another network represented as target model operating under other conditions. In TLF, a ResNet AE model will selectively outsource its pre-trained layers to a separate 1D-CNN model, which is a supervised learning model aimed to perform tasks, such as classification. In order to train both the source model and the target model, two separate databases are constructed using the Open Guided Waves diagnostic data repository containing scanned Lamb wave signals generated from a 2 mm thin carbon fibre-reinforced polymer plate structure, in which a range of frequencies and artificial defects are used. A TLF variant which includes transferred layers of pre-trained ResNet AE and 1D CNN classifier, have been developed, trained and tested with an unseen database containing 144 samples. Based on the test performance, the adopted version of TLF achieved an impressive 82.64% accuracy and emerged as the most robust, balanced and computationally more economical classification model.