Improving unsupervised long-term damage detection in an uncontrolled environment through noise-augmentation strategy

Autoencoder reconstruction-based unsupervised damage detection is widely utilized in structural health monitoring. However, such methods typically necessitate a comprehensive collection of historical guided waves as training data. Acquiring such data presents challenges, as it requires prolonged monitoring to cover various environmental and operational conditions (EOCs), making these methods less practical for real-world applications. This paper proposes an unsupervised damage detection method solely trained on the current measurements directly. To improve the performance of the unsupervised damage detection method when the training data (the current measurements ) contains a large ratio of damage-induced guided waves, two noise-augmentation strategies are designed to limit the neural network's learning ability to recover damage-induced guided waves from their segments, improving detection performance. Additionally, we use t-SNE to visualize the impact of noise augmentation on the separation of different types of guided waves within the recovery network's latent space. Experimental results indicate that input signals with relatively low SNR can achieve better damage detection performance, and a strategy for estimating the optimal noise intensity in input signals is provided in this paper. The effectiveness of the unsupervised this damage detection method with noise-augmentation strategy is validated by 10 regions of 80-days guided waves collected from uncontrolled and dynamic environmental conditions.