Electromechanical Impedance Data Fusion for Damage Detection

2021 ◽  
pp. 580-590
Author(s):  
Tomasz Wandowski ◽  
Pawel Malinowski
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Electromechanical Impedance ◽  
Impedance Data

scholarly journals A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4204
Author(s):  
Shishir Kumar Singh ◽  
Rohan Soman ◽  
Tomasz Wandowski ◽  
Pawel Malinowski
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Health Monitoring ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Damage Scenarios ◽  
Electromechanical Impedance ◽  
Fusion Approach

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.

Experimental Investigation on Improving Electromechanical Impedance Based Damage Detection by Temperature Compensation

2013 ◽  
Vol 569-570 ◽  
pp. 1132-1139 ◽  
Author(s):  
Thomas Siebel ◽  
Mihail Lilov
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Temperature Compensation ◽  
Impact Damage ◽  
Correlation Coefficients ◽  
Electromechanical Impedance ◽  
Reinforced Plastic ◽  
Influence Of Temperature On ◽  
Compensation Approach ◽  
The Impact

The sensitivity of the electromechanical impedance to structural damage under varying temperature is investigated in this paper. An approach based on maximizing cross-correlation coefficients is used to compensate temperature effects. The experiments are carried out on an air plane conform carbon fiber reinforced plastic (CFRP) panel (500mm x 500mm x 5mm) instrumented with 26 piezoelectric transducers of two different sizes. In a first step, the panel is stepwise subjected to temperatures between-50 °C and 100 °C. The influence of varying temperatures on the measured impedances and the capability of the temperature compensation approach are analyzed. Next, the sensitivity to a 200 J impact damage is analyzed and it is set in relation to the influence of a temperature change. It becomes apparent the impact of the transducer size and location on the quality of the damage detection. The results further indicate a significant influence of temperature on the measured spectra. However, applying the temperature compensation algorithm can reduce the temperature effect at the same time increasing the transducer sensitivity within its measuring area. The paper concludes with a discussion about the trade-off between the sensing area, where damage should be detected, and the temperature range, in which damage within this area can reliably be detected.

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Data Fusion ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Mixed Data ◽  
Image Encoding ◽  
Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

Method for removing temperature effect in impedance-based structural health monitoring systems using polynomial regression

2020 ◽  
pp. 147592172091712 ◽  
Author(s):  
Bárbara M Gianesini ◽  
Nicolás E Cortez ◽  
Rothschild A Antunes ◽  
Jozue Vieira Filho
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Temperature Effect ◽  
Health Monitoring ◽  
Monitoring Systems ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Detection Systems ◽  
Impedance Technique ◽  
Health Monitoring Systems

Structural health monitoring systems are employed to evaluate the state of structures to detect damage, bringing economical and safety benefits. The electromechanical impedance technique is a promising damage detection tool since it evaluates structural integrity by only measuring the electrical impedance of piezoelectric transducers bonded to structures. However, in real-world applications, impedance-based damage detection systems exhibit strong temperature dependence; therefore, variations associated with temperature changes may be confused as damage. In this article, the temperature effect on the electrical impedance of piezoelectric ceramics attached to structures is analyzed. Besides, a new methodology to compensate for the temperature effect in the electromechanical impedance technique is proposed. The method is very general since it can be applied to nonlinear (polynomial) temperature and/or frequency dependences observed on the horizontal and vertical shifts of the impedance signatures. A computer algorithm that performs the compensation was developed, which can be easily incorporated into real-time damage detection systems. This compensation technique is applied successfully to two aluminum beams and one steel pipe, minimizing the effect of temperature variations on damage detection structural health monitoring systems in the temperature range from −40°C to 80°C and the frequency range from 10 to 90 kHz.

An open database for benchmarking guided waves structural health monitoring algorithms on a composite full-scale outer wing demonstrator

2019 ◽  
Vol 19 (5) ◽  
pp. 1524-1541 ◽  
Author(s):  
Alessandro Marzani ◽  
Nicola Testoni ◽  
Luca De Marchi ◽  
Marco Messina ◽  
Ernesto Monaco ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Monitoring System ◽  
Health Monitoring ◽  
Guided Wave ◽  
Full Scale ◽  
Composite Panel ◽  
Health Monitoring System ◽  
Electromechanical Impedance ◽  
Structural Health

This article reports on the creation of an open database of piezo-actuated and piezo-received guided wave signals propagating in a composite panel of a full-scale aeronautical structure. The composite panel closes the bottom part of a wingbox that, along with the leading edge, the trailing edge, and the wingtip, forms an outer wing demonstrator approximately 4.5 m long and from 1.2 to 2.3 m wide. To create the database, a structural health monitoring system, composed of a software/hardware central unit capable of controlling a network of 160 piezoelectric transducers secondarily bonded on the composite panel, has been realized. The structural health monitoring system has been designed to (1) perform electromechanical impedance measurement at each transducer, in order to check for their reliability and bonding strength, and (2) to operate an active guided wave screening for damage detection in the composite panel. Electromechanical impedance and guided wave measurements were performed at four different testing stages: before loading, before fatigue, before impacts, and after impacts. The database, freely available at http://shm.ing.unibo.it/ , can thus be used to benchmarking, on real-scale structural data, guided wave algorithms for loading, fatigue, as well as damage detection, characterization, and sizing. As an example, in this work, a delay and sum algorithm is applied on the post-impact data to illustrate how the database can be exploited.

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