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|Title:||Autoregressive coefficients based Hotelling's T2 control chart for structural health monitoring||Authors:||Wang, Z.
Hotelling's T2 control chart
Multivariate statistical process control
Numerically simulated acceleration time histories
Structural health monitoring
Time series analysis
|Issue Date:||Oct-2008||Citation:||Wang, Z., Ong, K.C.G. (2008-10). Autoregressive coefficients based Hotelling's T2 control chart for structural health monitoring. Computers and Structures 86 (19-20) : 1918-1935. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compstruc.2008.02.007||Abstract:||This paper presents an innovative structural health monitoring (SHM) scheme based on time series analysis and multivariate statistical process control (MSPC) techniques. The scheme consists of two major procedures, viz vibration response data representation and characteristics monitoring. First, a series of autoregressive (AR) models are fitted to the response time histories of a structure to be monitored. Representing the health condition of the structure, the coefficients of these AR models are extracted to form a set of multivariate data known as vibration response data characteristics. Hotelling's T2 control chart is then applied to monitor these characteristics obtained. As an MSPC tool, Hotelling's T2 control chart has the capacity of simultaneously monitoring the multivariate characteristics data without having to neglect the inherent relation between the components of the data. The efficacy of the proposed SHM scheme is demonstrated by numerically simulated acceleration time histories based on a progressively damaged reinforced concrete (RC) frame, either with or without addressing the autocorrelation in the characteristics data. The results are compared with those obtained by using univariate Shewhart over(X, -) control chart to show the advantages of the proposed scheme in terms of the sensitivity of the defined damage indicator with respect to damage severity. A parametric study is also included to investigate the effects of the number of data points used for AR model fitting, the order of AR models, the number and location of sensors on the proposed scheme and to further illustrate its potential as a promising SHM approach. © 2008.||Source Title:||Computers and Structures||URI:||http://scholarbank.nus.edu.sg/handle/10635/84528||ISSN:||00457949||DOI:||10.1016/j.compstruc.2008.02.007|
|Appears in Collections:||Staff Publications|
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