Comparative Analysis between Singular Spectral Analysis and Empirical Mode Decomposition for Structural Damage Detection

Abstract

Context: In recent years, thanks to technological advances in instrumentation and digital signal processing, noninvasive methods to detect structural damage have become increasingly important. Vibration-based structural health monitoring (SHM) techniques allow detecting the presence and location of damage from permanent changes in the fundamental frequencies of signals. A successfully employed method for damage detection is empirical mode decomposition (EMD). Another method, less used in this field of study, is singular spectral analysis (SSA). This paper describes both methods and presents a simulation study aimed at comparing them and identifying which one is more effective in detecting structural damage. Method: The methods of a reference study known as benchmark SHM were applied to facilitate the comparison. To evaluate the effectiveness of both methods, Monte Carlo simulation was employed. To control the random noise and other factors inherent to the simulation, the procedure was repeated 1.000 times for each type of damage. Results: In the case of severe damage, both methods showed a good performance. However, when the damage was slight, the changes in the fundamental frequency were not apparent. However, a significant change in the amplitude level was observed. In this case, SSA obtained the best results. Conclusions: The EMD and SSA methods, together with high-pass filtering, detected severe damage when the acceleration records had low or no noise. When the acceleration records were contaminated with noise, the likelihood of EMD detecting the damage decreased dramatically. One of the advantages of SSA over EMD is that, for moderate or mild damage patterns, the former does not require filters or the use of the Hilbert-Huang transform to detect damage. In general, it was found that SSA was more effective in detecting damage.