Coherence Analysis for Vibration Monitoring Under High Variability Conditions: Constraints for Cultural Heritage Preventive Conservation

The development of reliable sensor networks for vibration monitoring is essential for the preventive conservation of buildings and structures. The identification of natural frequencies is crucial both for sensor network planning, to ensure optimal placement, and for operation, to detect frequency shifts that may indicate structural damage. However, traditional frequency detection methods, such as peak picking of the Spectrum or Power Spectral Density (PSD), are highly dependent on structural and environmental conditions. In highly variable vibrational environments, such as cultural heritage sites, stadiums, and transportation hubs, these methods often prove inadequate, leading to false modal identification. This study applies coherence analysis to vibrational measurements as a more reliable alternative that overcomes the limitations of traditional frequency extraction techniques. To evaluate its effectiveness, Magnitude-Squared Coherence (MSC), Squared Cross-Spectrum (SCS), and Wavelet Coherence (WC) were tested and compared with PSD analysis. Vibrational data were collected from a sensor network deployed at the Civil Museum of Castello Ursino (Catania, Italy), a site characterized by high structural complexity and variable visitor-induced vibrations. Results demonstrate that coherence analysis surpasses the limitations of traditional frequency identification techniques, with SCS and WC outperforming MSC in distinguishing resonance frequencies and providing a more stable and reliable frequency estimation. This approach enhances sensor network design by improving frequency detection, ensuring data reliability, and supporting longterm monitoring through instrumental drift detection, thus strengthening structural health monitoring in heritage sites.