Guided waves (GW) allow fast inspection of a large area and hence have attracted research interest from the structural health monitoring (SHM) community. Thus, GW-based SHM is ideal for thin structures such as plates, pipes, etc., and is finding applications in several fields like aerospace, automotive, wind energy, etc. The GW propagate along the surface of the sample and get reflected from discontinuities in the structure in the form of boundaries and damage. Through proper signal processing of the reflected waves based on their time of arrival, the damage can be detected and isolated. For complex structures, a higher number of sensors may be required, which increases the cost of the equipment, as well as the mass. Thus, there is an effort to reduce the number of sensors without compromising the quality of the monitoring achieved. It is of utmost importance that the entire structure can be investigated. Hence, it is necessary to optimize the locations of the sensors in order to maximize the coverage while limiting the number of sensors used. A genetic algorithm (GA)-based optimization strategy was proposed by the authors for use in a simple aluminum plate. This paper extends the optimization methodology for other shape plates and presents experimental, analytical, and numerical studies. The sensitivity studies have been carried out by changing the relative weights of the application demands and presented in the form of a Pareto front. The Pareto front allows comparison of the relative importance of the different application demands, and an appropriate choice can be made based on the information provided.

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.

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

Electromechanical impedance (EMI) based techniques have been proposed for structural health monitoring due to their sensitivity to low levels of damage. Most of the work in the EMI technique depends on the change in the admittance signature of the structure in the healthy and damaged state. Several metrics have been proposed to quantify this difference in the signature. Most common being root-mean square difference (RMSD), mean absolute percentage deviation, correlation coefficient etc. As the admittance signatures has several troughs and peaks, the statistical metrics are not robust and often show false detection due to ambient changes and measurement noise. Thus, this paper proposes a novel index for the damage detection using the EMI technique based on the cumulative electrical power. The frequency v/s resistance or conductance plot is used for calculating the normalized cumulative electrical power (NCP) of the system. The NCP curve is a monotonically increasing function and hence robust for statistical comparison. The cumulative power curve is then used to develop three different indices comparing the amplitude difference (RMSD of the NCP curves), difference in the area under the NCP curve as well as the modified Frechet distance between the NCP curves. The performance of these indices are compared with the RMSD index which has been commonly used. The comparison is carried out on four different structures and show very encouraging results. In addition to the experimental validation, sensitivity studies have been carried out on an analytical signal. It is seen that the Frechet distance based index is a robust indicator for damage detection and minimizes the false detection under variety of conditions affecting the EMI signature.

Electromechanical impedance (EMI) measurement, using piezoelectric transducers (PZTs) in the high-frequency range is a potential method for assessing the health of lightweight structures. The major objective of this work is to comprehend how different actuators react to damage in additively manufactured (AM) polymer structures. A novel frequency-range selection technique was suggested based on the maxima of the standard deviation of the impedance frequency spectra gathered for the referential and damage cases. A 3D-printed acrylonitrile butadiene styrene (ABS) plate was used for the investigation, where two PZT and one macro fiber composite (MFC) actuator were glued to the surface. Small magnets were used to simulate damage and were positioned at increasing distances from each transducer as EMI measurements were made using the MFC and 1 PZT. This served both in studying the transducers’ sensitivity to damage and selecting the proper frequency range for damage detection utilizing the standard-deviation approach. The EMI-acquired data from the MFC actuator displays damage-sensitive peaks in a low-frequency band (0–58 kHz ), while the PZT shows a good sensitivity in a higher frequency range (94–304 kHz ). In order to evaluate the PZT and MFC actuators’ sensitivity to damage in the 3D-printed ABS plate, impact damage is also generated in the plate’s center. The impedance-based damage indices obtained from different types of PZTs (2 PZTs and 1 MFC) were projected to the same base level and then fused—for the first time—for impact-damage localization and further added magnetic mass damage localization. The obtained damage index values of impedance are encouraging for the evaluation of AM polymer structures with a 4.48 mm positional error from a real location by fusing data in the different frequency ranges for PZTs and MFC. The damage localization error increases significantly to the new location beyond the damage sensitivity range of the PZT2 and MFC for the added magnetic mass on the 3D printed structure.