A Vibration Measurement Data Enhancement Approach Based on Variational Autoencoders for Structural Health Monitoring

Structural Health Monitoring (SHM) increasingly relies on data-driven approaches to detect structural changes under environmental and operational variability, yet the limited availability and imbalance of baseline data remain critical challenges. This study proposes a novel framework for vibration-based SHM that combines Convolutional Neural Networks and Variational Autoencoders to model structural response in the frequency domain through Cross-Spectral Matrices. The methodology includes a tailored data representation based on Cholesky factorisation, a CNN-VAE architecture with structural constraints to ensure data consistency, and an Enhanced Loss Function designed to improve sensitivity to modal characteristics. The trained model is used both as a generative tool to produce realistic synthetic data and as a feature extractor through latent variable distributions. Validation on an experimental truss structure subject to thermal variability shows that the model accurately reproduces the statistical distribution of natural frequencies and spectral features, while generating plausible synthetic responses. The proposed approach enables baseline enhancement through data balancing and supports effective damage detection using both modal features and latent space indicators. These results demonstrate that the framework can improve the robustness of vibration-based SHM systems and can be integrated with existing frequency domain monitoring techniques, offering a practical data-driven solution for real-world applications.