Lamb wave-based Structural Health Monitoring (SHM) is a promising technique for detecting defects in materials and structures. However, traditional methods often rely on computationally intensive signal processing and struggle to detect subtle anomalies wave patterns. In this work, we propose a novel transformer-based framework, called Dual-Contrastive-Attention Transformer (DCAT), for unsupervised anomaly detection in Lamb wave data. DCAT uses two attention branches during training: a Global-Context Attention (GCA) branch that captures long-range patterns, and a Local-Context Attention (LCA) branch that serves as a constraint. A contrastive loss is used to prevent the global branch from over-learning local features, encouraging it to focus on the overall structure. Both branches are trained to reconstruct the input, using a structural similarity (SSIM) loss that better reflects waveform patterns than traditional mean squared error. After training, only the global branch is retained for inference. Anomalies are detected by comparing the input and reconstructed output. Since the global branch cannot easily reproduce local defects, it produces a higher SSIM loss when anomalies are present. We test our model on a Lamb wave dataset with multiple types of defects. DCAT achieves 97.8% accuracy and a precision of 98.6%, outperforming other SOTA baselines. These results show that DCAT is well-suited for accurate Lamb wave-based SHM without the need for labeled data.
Guo et al. (Sun,) studied this question.