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 on 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 data clearly distinguishing data sets obtained from different structural conditions.
Structural Health Monitoring Using Statistical Pattern Recognition Techniques
Contributed by the Dynamic Systems and Control Division for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the Dynamic Systems and Control Division February 7, 2001. Associate Editor: S. Fassois.
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Sohn, H., Farrar, C. R., Hunter, N. F., and Worden, K. (February 7, 2001). "Structural Health Monitoring Using Statistical Pattern Recognition Techniques ." ASME. J. Dyn. Sys., Meas., Control. December 2001; 123(4): 706–711. https://doi.org/10.1115/1.1410933
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