Singular spectrum analysis combined with ARMAX model for structural damage detection

Time series analysis is being used popularly in structural health monitoring mainly because of its output-only and non-modal approach. Generally, the damage features are extracted either from the coefficients or the prediction errors of the time series models. However, when the incipient damage is small like minor cracks, the damage features of popularly used time series models, constructed using only the coefficients/prediction errors, are not sensitive. Therefore, identifying the presence or exact spatial damage location becomes difficult. In view of this, in this paper, we present an approach to enhance the sensitivity of the damage features by augmenting Singular Spectrum Analysis (SSA) to ARMAX model, enabling it to locate the smaller damage like cracks. The damage index is obtained from the Cepstral distance between any two ARMAX models. Numerical simulation studies have been carried out by considering an example of a simply supported beam girder with single and multiple cracks. Experimental studies on a simply supported RCC beam is conducted to demonstrate the effectiveness of the proposed algorithm. A benchmark problem associated with the bookshelf frame structure, proposed by Engineering Institute -Los Alamos National Laboratory, is used as another example for experimental verification of the proposed technique. SSA is found to improve the sensitivity of the damage features devised from the ARMAX models for detection of minor damage and damage localization on the structures.