Singular spectrum analysis and its application in Lamb wave-based damage detection

This paper proposes singular spectrum analysis (SSA) based feature extraction method in Lamb wave based damage detection. SSA is used for the decomposition of the acquired Lamb wave signal into an additive set of principal components and a new universal approach for selection of the principal components is presented. The principal components which contain the least measurement noise and the most damage information are then used to detect local damage in an aluminum plate and a new approach based on the maximum likelihood analysis for damage signal decomposition is proposed. Genetic algorithm is adopted for the purpose of making the similarity between the synthetic signal and the target signal reach the maximum. The experimental result shows that the proposed method is capable of yielding accurate identified results with noisy measurement

Electricity consumption forecasting using singular spectrum analysis

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil