An adversarial transformer for anomalous lamb wave pattern detection.

Abstract

Lamb waves are widely used for defect detection in structural health monitoring, and various methods are developed for Lamb wave data analysis. This paper presents an unsupervised Adversarial Transformer model for anomalous Lamb wave pattern detection by analyzing the spatiotemporal images generated by a hybrid PZT-scanning laser Doppler vibrometer (SLDV). The model includes the global attention and the local attention mechanisms, and both are trained adversarially. Given the different natures between the normal and anomalous wave patterns, global attention allows accurate reconstruction of normal wave data but is less capable of reproducing anomalous data and, hence, can be used for anomalous wave pattern detection. Local attention, however, serves as a sparring partner in the proposed adversarial training process to boost the quality of global attention. In addition, a new segment replacement strategy is also proposed to make global attention consistently extract textural contents found in normal data, which, however, are noticeably different from anomalies, leading to superior model performance. Our Adversarial Transformer model is also compared with several benchmark models and demonstrates an overall accuracy of 97.1 % for anomalous wave pattern detection. It is also confirmed that global attention and local attention in adversarial training are responsible for the superior performance of our model over the benchmark models (including the native Transformer model).