Improved Single-Sensor-Based Modal Identification Using Singular Spectrum Analysis

The present study aims to demonstrate the capabilities of singular spectrum analysis (SSA) as a filter bank that could potentially be integrated into a modal identification framework using single-sensor output information. SSA reconstructs the original time series using principal components from the signal subspace—thereby eliminating noise components altogether—and yields a filtered signal that finds its use in modal identification. Using time-domain decomposition and least-squares technique, the modal response generated using SSA is fit with a free-decay signal from which the estimates of natural frequencies and damping ratios are carried out. The paper attempts to investigate a simple model of a dynamical system and analyzes it using the proposed approach to provide a comparison with traditional operational modal analysis techniques. The results demonstrate that SSA can be used as a powerful tool for the analysis of vibratory behavior for structures exhibiting well-separated spectral components. Inherent filtering embedded within its framework and its seamless integration into a modal identification framework hold strong promise for application inbuilt infrastructure systems.