Structural health monitoring using statistical pattern recognition techniques

被引:308
作者
Sohn, H
Farrar, CR
Hunter, NF
Worden, K
机构
[1] Los Alamos Natl Lab, Engn Sci & Applicat Div, Engn Anal Grp, Los Alamos, NM 87545 USA
[2] Los Alamos Natl Lab, Engn Sci & Applicat Div, Measurement Technol Grp, Los Alamos, NM 87545 USA
[3] Univ Sheffield, Dept Mech Engn, Sheffield S1 3JD, S Yorkshire, England
来源
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2001年 / 123卷 / 04期
关键词
D O I
10.1115/1.1410933
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surface-effect fast patrol boat. The first technique is based (oil a two-stage time series analysis combining Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The second technique employs an outlier analysis with the Mahalanobis distance measure. The main objective is to extract features and Construct a statistical model that distinguishes the signals recorded under the different structural conditions of the boat. These two techniques were successfully applied to the patrol boat different structural Conditions, data clearly distinguishing data sets obtained from different structural conditions.
引用
收藏
页码:706 / 711
页数:6
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