Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart

Wang, Zengrong, and Ong, K.C.G. (2009) Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart. Engineering Structures, 31 (5). pp. 1265-1275.

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Abstract

A novel structural damage detection scheme using autoregressive-model-incorporating multivariate exponentially weighted moving average (MEWMA) control chart is presented in this paper. This scheme comprises procedures based on the undamaged or reference state of the structure being monitored and those based on its damaged or current state. In the procedures based on the reference state, sets of multivariate data are formulated by a series of autoregressive (AR) model fitting, and these data are then subjected to MEWMA control chart analysis to establish a benchmark damage indicator. The damage indicator obtained in the procedures based on the current state is compared with the benchmark for the purpose of structural damage detection. The autocorrelation in the multivariate data is addressed, and special procedures to allow for the uncertainty involved in process parameter estimation as well as those for control limit determination are proposed for structural damage detection application. A numerically simulated case study is used to verify the efficacy of the proposed scheme and to show its advantages. A parametric study is also included to study the effects of some parameters and to demonstrate the robustness of the scheme against parameter selection.

Item ID: 19218
Item Type: Article (Refereed Research - C1)
Keywords: structural health monitoring; damage detection; autoregressive; statistical models; statistical process control; multivariate exponentially weighted moving average control chart
ISSN: 1873-7323
Date Deposited: 21 Nov 2011 02:56
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090506 Structural Engineering @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100%
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