FAME–SSA: a Fast Adaptive Multivariate Decomposition Method for unsupervised damage detection in built infrastructures

Structural Health Monitoring (SHM) techniques are continuously challenged by the complexities and noise inherent in large-scale multidimensional data. Empirical Mode Decomposition (EMD) is effective for non-stationary signals but struggles with multichannel data. Multivariate EMD (MEMD) addresses this but still suffers from noise sensitivity, mode mixing, and incomplete frequency extraction. Fast and Adaptive Multivariate EMD (FA-MVEMD) improves on MEMD by using intelligent strategies to enhance accuracy and performance. This study introduces a novel methodology for modal identification and damage detection: Fast and Adaptive Multivariate Empirical Singular Spectrum Analysis (FAME-SSA). By combining the adaptive decomposition capability of FA-MVEMD with the trend extraction and noise separation strengths of Singular Spectrum Analysis (SSA), the proposed approach improves the accuracy and robustness of feature extraction from structural responses. A key innovation of FAME-SSA is the application of Hotelling’s T² and Squared Prediction Error (SPE) statistics for damage detection. The method is validated through extensive numerical simulations, experimental data from a wind turbine structure, and real-world SHM data from the Lysefjord Bridge. The results demonstrate that FAME-SSA outperforms conventional methods, making it a promising tool for real-time SHM in complex and noisy environments under challenging conditions.