Open AccessAn optimized data fusion strategy for structural damage assessment using electromechanical impedance
Author(s) -
Shishir Kumar Singh,
Shirsendu Sikdar,
Paweł Malinowski
Publication year - 2021
Publication title -
smart materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.898
H-Index - 154
eISSN - 1361-665X
pISSN - 0964-1726
DOI - 10.1088/1361-665x/abdc07
Subject(s) - structural health monitoring , principal component analysis , sensor fusion , electrical impedance , sensitivity (control systems) , frequency domain , emi , standard deviation , range (aeronautics) , piezoelectric sensor , time domain , pattern recognition (psychology) , engineering , computer science , statistics , acoustics , mathematics , electronic engineering , artificial intelligence , structural engineering , piezoelectricity , electromagnetic interference , physics , electrical engineering , computer vision , aerospace engineering
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.