Order Estimation of Computational Models for Dynamic Systems with Application to Biomedical Signals

Cassar, Tracey A.; Camilleri, Kenneth P.; Fabri, Simon G.

doi:10.1109/advcomp.2009.33

Cited by 3 publications

(2 citation statements)

References 5 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It will result in a poor AR model prediction and will result in a false alarm of damage. 68,69 The higher model order will fit the unwanted error/noise in the data, and it will lead to inaccurate results and conclusions whereas the lower model order would not be able to capture the system dynamics properly, again, resulting in false interpretation.…”

Section: Selection Of Optimal Model Order For Adaptive Ar Modellingmentioning

confidence: 99%

Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading

Rajeev

Pamwani

Ojha

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

In the present work, a novel technique based on the combination of singular spectral analysis (SSA) and recursive estimate of coefficients of adaptive autoregressive (AR) modelling is employed to identify the damage in the reinforced (RC) beam-column joints. The damage is induced by imparting shock load at the tip of the beam-column joints. The damage is identified with the help of the acceleration response of the healthy and damaged specimens excited by high intensity white noise. The proposed approach has two major components, first, filtering and removing the noise from the dynamic response using the singular spectral analysis and second, modelling the filtered response using adaptive AR process to get the recursive estimate of coefficient matrix for baseline and damage states. The coefficients evaluated for each time instant are presented in a multi-dimensional subspace to form distinct clusters corresponding to a healthy and damaged state. In order to identify and quantify the damage, the geometrical and statistical measures are evaluated that quantifies the segregation of clusters. In total, three distinct measures are used to quantify the damage, namely, Euclidean distance (ED), Mahalanobis distance (MD) and Bhattacharyya distance (BD). The BD accounts the variation in the distribution of both the clusters, thereby shows superior results comparatively than ED and MD. The results of DSFs also manifest the superiority of BD over the other two DSFs. These geometrical and statistical distances are the damage sensitive feature (DSF) to identify and quantify the damage in the specimen due to the shock load. The obtained results of all the DSFs show good consistency with the maximum deformation of the specimen due to shock loading highlighting the accuracy of the proposed algorithm.

show abstract

Section: Selection Of Optimal Model Order For Adaptive Ar Modellingmentioning

confidence: 99%

Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading

Rajeev

Pamwani

Ojha

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Model order estimation remains a very complex issue, and various techniques to address this problem have been proposed. Based on the mathematical formulation, the techniques can be classified into three major categories (Cassar et al, 2009). The first category requires an a priori estimate of the model parameters to find the optimal order.…”

Section: Feature Extraction: the Autoregressive Modelmentioning

confidence: 99%

Influence of the Autoregressive Model Order on Damage Detection

Figueiredo

Figueiras

Park

et al. 2010

Computer-Aided Civil and Infrastructure Engineering

115

View full text Add to dashboard Cite

An important step for using time-series autoregressive (AR) models for structural health monitoring is the estimation of the appropriate model order.To obtain an optimal AR model order for such processes, this article presents and discusses four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition. A unique contribution of this work is to provide a comparative study with three different AR models that is carried out to understand the influence of the model order on the damage detection process in the presence of simulated operational and environmental variability. A three-story base-excited frame structure was used as a test bed in a laboratory setting, and data sets were measured for several structural state conditions. Damage was introduced by a bumper mechanism that induces a repetitive impact-type nonlinearity. The operational and environmental effects were simulated by adding mass and by changing the stiffness properties of the columns. It was found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the * To whom correspondence should be addressed. E-mail: farrar@ lanl.gov optimal model order. The comparative study carried out on these data sets shows that the AR model order range defined by the four techniques provides robust damage detection in the presence of simulated operational and environmental variability.

show abstract