Singular spectrum analysis combined with ARMAX model for structural damage detection

Abstract

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.

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

“…The method has been employed and shown its capabilities for noise reduction for longitudinal measurements and surface roughness monitoring [26,30]. It has also been implemented for structural damage detection [36]. The superiority of the SSA over other methods in biomechanical analysis was clearly demonstrated by several examples presented in the work in [37].…”

Modified dam deformation monitoring model considering periodic component contained in residual sequence

“…In addition, considering that the residual sequence contains certain noise and periodic components, a modified modeling method by extracting periodic component from the residual sequence of conventional statistical model is proposed in the study. Inspired by the application of singular spectrum analysis (SSA) in data processing [27][28][29][30] and the application of autoregressive integrated moving average (ARIMA) model in time series analysis, [31][32][33][34] the residual sequence obtained by conventional statistical model is processed and forecasted by SSA and ARIMA. Firstly, the conventional statistical model is established with stepwise regression model, and the residual sequence is reconstructed using the trend and periodic components extracted by SSA.…”

Vibration‐based damage localization with load vectors under temperature changes

“…For example, damage localization can be performed on the basis of changes in natural frequency, mode shapes or modal curvature, or modal strain energy . Furthermore, artificial neural networks, genetic algorithms, wavelet‐based analysis, or other signal processing methods are applied among others. Besides model‐based and data‐driven approaches, a third group of methods has emerged that combines properties of both approaches, using data‐driven features computed in the reference and damaged states as well as information from an FE model of the healthy structure .…”