An optimized data fusion strategy for structural damage assessment using electromechanical impedance

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 classif...

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Published inSmart materials and structures Vol. 30; no. 3; pp. 35012 - 35026
Main Authors Singh, Shishir K, Sikdar, Shirsendu, Malinowski, Pawel H
Format Journal Article
LanguageEnglish
Published IOP Publishing 01.03.2021
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Abstract 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 T2 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.
AbstractList 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 T2 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.
Abstract 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.
Author Sikdar, Shirsendu
Singh, Shishir K
Malinowski, Pawel H
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  surname: Malinowski
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Snippet This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using electromechanical...
Abstract This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using...
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SubjectTerms data fusion
electromechanical impedance
principal component analysis
self-organizing map
sensor network
structural health monitoring
Title An optimized data fusion strategy for structural damage assessment using electromechanical impedance
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