期刊
SMART MATERIALS AND STRUCTURES
卷 30, 期 3, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/1361-665X/abdc07
关键词
electromechanical impedance; structural health monitoring; sensor network; data fusion; self-organizing map; principal component analysis
资金
- National Science Center, Poland [2016/22/E/ST8/00068]
- Research Foundation-Flanders (FWO), Belgium [FWO.3E0.2019.0102.01]
This paper introduces a new sensor network optimized data fusion approach for monitoring the health of metallic structures using EMI signals. The approach integrates common baseline models, PCA, and SOMs to study piezoelectric transducer parameters in the frequency domain. The proposed method shows robust damage sensitivity for hole locations and enlargement regardless of the frequency range selected.
This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using electromechanical impedance (EMI) signals. The integrated approach used to fuse common healthy state baseline model based damage detection, quantification and classification in EMI technique. Towards this, the principal component analysis (PCA) is carried out and corresponding the root mean square deviation (RMSD) index is calculated to study the information of piezoelectric transducer's impedance (|Z|), admittance (|Y|), resistance (R), and conductance (G) in the frequency domain. A new optimized data fusion approach is proposed which was realized at the sensor level using the PCA as well as at the variable level using self-organizing maps (SOMs). The SOM comparative studies are performed using the Q-statistics (Q index) and the Hotelling's T-2 statistic (T index). The proposed methodology is tested and validated for an aluminum plate with multiple drilled holes with variable size and locations. In the process, a centralized data-fused baseline eigenvector is prepared from a healthy structure and the damage responses are projected on this baseline model. The statistical, data-driven damage matrices are calculated and compared with the RMSD index and used in a fusion based data classification using SOM. The proposed method shows robust damage sensitivity for hole locations and hole enlargement irrespective of the wide frequency range selection, and the selected frequency range contains the resonant frequency range.
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