Electromechanical Impedance Research Articles

Numerical Analysis of Steel Frame Using Electro-Mechanical Impedance Technique

Nowadays, piezoelectric materials are most widely used in structural health monitoring (SHM) system for nondestructive evaluation. In this paper the damage study using electro-mechanical impedance (EMI) technique, which is nondestructive method of SHM. This paper presents a numerical analysis of four-story steel frame and multiple damages were induced in the steel frame by utilizing smart material like Lead Zirconate Titanate (PZT). A PZT-4 piezoelectric material was embedded in the beam of the frame. The type of damping used in steel frame model was Rayleigh damping. The numerical analysis was done in COMSOL MULTIPHYSICS 6.2 and frequency domain analysis was done, from that admittance values were find out for 30-300KHz frequency range. The real part of the admittance gives conductance values. Then, conductance verses frequency graph was created that will indicate the structural health and performance. Moreover, root mean square deviation (RMSD) index was found out for each case that provides a quantitative measure of deviation.

Entire loosening stage monitoring of bolted joints via nonlinear electro-mechanical impedance spectroscopy

This article proposes a nonlinear electro-mechanical impedance spectroscopy (NEMIS) methodology for the entire loosening stage monitoring of bolted joints. Unlike the conventional electro-mechanical impedance spectroscopy (EMIS) implemented in the frequency domain, NEMIS utilizes a temporal interrogating signal to procure the developed nonlinear impedance spectrum. It can harness the structural resonance information akin to the conventional EMIS, while concurrently capturing the contact acoustic nonlinearity (CAN) features with only one solitary Piezoelectric Wafer Active Sensor (PWAS). To elucidate the underlying principles of NEMIS, a comparative analysis juxtaposing the conventional EMIS, nonlinear ultrasonic techniques, and the proposed NEMIS was conducted. Subsequently, a reduced-order analytical model was established to simulate the rough contact interfaces of the bolted joints under various looseness states. Parametric studies were carried out to demonstrate the resonance deviation and nonlinear features resulting from the bolt looseness. Ultimately, the experimental implementation of NEMIS for the entire loosening stage monitoring of the bolted joint was performed compared with the conventional EMIS method. It was proved that NEMIS could detect both the incipient bolt loosening at an embryo stage and severe loosening of the bolted joint at the terminal period, integrating the continuous monitoring capability of conventional EMIS and the sensitivity of nonlinear ultrasonic methodology. The quantification on the severity of bolt loosening was accomplished via both linear and nonlinear damage indices, exhibiting a consistent trend with the gradations of bolt looseness. The article culminates in a summary, concluding remarks, and suggestions for future work.

Visualising the strength development of FICP-treated sand using impedance spectroscopy

Fungal Induced Calcium Carbonate Precipitation (FICP) is a novel method used in geotechnical engineering that enhances the engineering properties of sand by using the potential of fungal activity. This research is the first attempt to monitor the strength of FICP treated sand using embedded Piezoelectric (PZT) patch based Electromechanical Impedance (EMI) spectroscopy. In the past, the strength of such treated sand has been determined through the destructive methods like Unconfined Compressive Strength (UCS) test. In this study, the sand is mixed with the filamentous fungus Aspergillus Niger and the cementation solution (urea and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{C}\ ext{a}\ ext{C}\ ext{l}}_{2}$$\\end{document} in the ratio of 1:1) is injected after every 24 h. Results recorded from the cost-effective EVAL AD5933 chip indicate that the shifting of frequency impedance signals in each phase is in good alignment with UCS and calcium carbonate content (CCC). Following the 28-day treatment period, the treated sand achieves a maximum UCS of 3.93 MPa, accompanied by a CCC of 15.19%. In order to correlate EMI signals with treatment cycles, UCS, and CCC, various multi linear regression (MLR) equations for statistical metrics like root mean square deviation (RMSD), mean absolute percentage deviation (MAPD), and correlation coefficient deviation (CCD) are developed. Additionally, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analyses have been conducted to observe the success of the FICP process in the sand.

Void detection in structural adhesive joints using a k-Nearest Neighbors model with features from Electromechanical Impedance Spectroscopy

Electromechanical Impedance Spectroscopy (EMIS) is a promising Structural Health Monitoring (SHM) method which allows the early detection of defects by analyzing the structure response to an AC electrical signal swept through a range of high frequencies. In this work, EMIS measurements of pristine and damaged adhesive joints were performed, and features were extracted from the experimental measurements. These features were inputted to a k-Nearest Neighbors (kNN) model for damage detection. Results show that only one type of features is enough for damage detection. Furthermore, the use of the Manhattan Distance in the kNN enables a better classification.

The Influence of Coupling-Specific Parameters and Structural Parameters on the Electromechanical Impedance of Acoustic Emission Sensors

Acoustic emission (AE) sensors are widely used for testing and monitoring purposes, necessitating effective field-testing methods to assess their functionality during use, ideally resulting in a method for self-diagnostic. A novel approach for field-testing is to measure the electromechanical impedance (EMI) of the AE sensor. This method requires minimal additional equipment and eliminates human interaction, which is needed for current field-tests like pencil-lead break tests. However, the EMI includes the mechanical impedances of the sensor, the coupling layer, and the structure. Therefore, the measurements contain information about all three and influences of the structure and the coupling layer need to be investigated to be able to establish a method for self-diagnosis in field. Additionally, the piezoelectric effect is temperature-dependent, which affects the measured EMI spectra and may result in erroneous assessments of the sensor's condition if not accounted for. This work presents experimental studies on the influence of measuring equipment, environmental temperature, coupling-specific parameters and structural parameters on the EMI of AE sensors. Moreover, numerical simulations have been conducted to provide additional support and to extend the experimental studies of the latter two. To ensure reproducible sensor coupling to a structure, a setup was constructed to couple the sensor to the structure at the same position and with the same force. The thickness of the coupling layer is determined by a small amount of silicon carbide particles with a specific diameter. This setup is also used to examine the effect of structural parameters.

Aging State Evaluation of Oil-Paper Insulation Based on Electro-Mechanical Impedance Technology

Aging State Evaluation of Oil-Paper Insulation Based on Electro-Mechanical Impedance Technology

Temperature compensation method for impedance signals of bolt loosening under temperature varying conditions using TransUnet

Abstract Piezoelectric transducer based electro-mechanical impedance technology is an effective and reliable detection method for bolt structure loosening under external load. However, temperature change usually causes some non-loosening factors to change the impedance spectrum of bolt structure. In order to reduce the misjudgment of loosening damage caused by temperature change, it is necessary to construct a temperature compensation model for impedance spectrum. In this paper, the convolutional structure in U-net and Transformer are effectively combined to form a TransUnet deep neural network structure suitable for input and output of one-dimensional data. Using impedance data between 10 °C and 50 °C and temperature as input to the network. After convolution operation, the convolutional block attention module is embedded in the U-net to optimize the encoder transmission characteristics and enhance the performance of the skip connection in the traditional U-shaped structure. The temperature compensation rate (TCR) is defined to measure the effect of temperature compensation. Then the lightweight convolutional neural network structure is used to recognize the bolt loosening damage of the compensated impedance signal. The generalization ability of the TransUnet was tested using impedance data that is not in the training dataset. The results show that the TransUnet proposed in the paper can realize the temperature compensation of multi-peak impedance signals. The TCR and recognition accuracy of bolt loose damage reaches reach 0.003 and 95.7%, respectively, which is 11% higher than that without temperature compensation. At 60 °C, the TCR and the identification accuracy of loosening damage can still reach 0.0055 and 90.2%, respectively, which show that the TransUnet has strong generalization ability.

Assessing cement paste strength evolution under curing: An experimental and numerical investigation through equivalent stiffness parameter identified by embedded piezo sensors

Equivalent stiffness is one of the most important mechanical parameters of any concrete structure which allows engineers to predict the behaviour of the structure. This paper identifies the equivalent stiffness parameter through an embedded piezo sensor (EPS) in the strength development of cement paste using the electro-mechanical impedance (EMI) technique through experimental and numerical investigation. The experiments were conducted on the cement paste specimens; and the compressive strength (destructively) and EMI response were obtained during curing process. Further, a numerical model of cement paste specimen with EPS is developed to validate the EMI response. In addition, an equivalent stiffness parameter is identified from the experimental and simulation EMI data. It is found that the variation in conductance signature during curing process and strength development is effectively captured by the EPS. The identified equivalent stiffness either calculated from experimental or simulation data followed a similar behaviour of the compressive strength.

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

A sandwich-structured 1-3 type piezoelectric composite material for enhancing reliability

This study proposes a sandwich structure 1-3 piezoelectric composite, incorporating flexible silicone rubber to enhance the thickness vibration mode of piezoelectric ceramics. Rigid aluminum nitride (AlN) is employed to bolster the mechanical stability of the composite. Utilizing equivalent parameter theory, we establish a mathematical model for the sandwich structure composite material. The impact of the piezoelectric phase size and the volume fraction of AlN on several performance parameters of the material are analyzed, including the thickness electromechanical coupling coefficient, density, sound velocity, and characteristic impedance. The sandwich structure piezoelectric composite was fabricated using the "cutting and filling" technique. Experimental outcomes reveal that this material demonstrates concentrated thickness vibration modes, with a thickness electromechanical coupling coefficient reaching as high as 0.72, a 16.6 % increase compared to traditional 1-3 piezoelectric composites. Results from thermal shock tests and tensile tests suggest that the sandwich structure 1-3 piezoelectric composite has considerable potential for use in highly reliable low-frequency receiving transducers.

Corrosion evolution and quantitative corrosion monitoring of Q355 steel for offshore wind turbines in multiple marine corrosion zones

Offshore wind turbines operating in harsh marine environments are at risk of corrosion. Thickness loss and perforation caused by corrosion can lead to component failure or structure collapse. An environmental test setup was constructed to simulate various marine corrosion zones. Several experimental studies were conducted on Q355 steel, commonly used in offshore wind turbines. The mass loss and electrochemical properties of the steel specimens were analyzed, including electrochemical impedance spectra and polarization curves. The results indicate that corrosion remains stable in the immersion and tidal zones, whereas corrosion in the splash zone is the most severe, with a corrosion rate as high as 0.70 mm/a. A significant macroscopic corrosion cell effect was observed on the vertical steel strip, accelerating corrosion in the splash zone and at the low tide line. The corrosion product compositions and corrosion morphology of each corrosion zone were characterized and analyzed. Furthermore, a corrosion monitoring sensor based on electromechanical impedance was proposed. A prediction model for the mass loss rate of vertical steel strips was developed based on the conductance peak frequency. This study elucidates the corrosion evolution of wind turbine structural steel and proposes a comprehensive steel corrosion monitoring solution.

Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems

In recent years, the assessment of structural integrity and durability of concrete systems has significantly advanced with the integration of novel sensing technologies and data-driven techniques. This study introduces an innovative approach that utilizes electro-mechanical impedance (EMI) data acquired through embedded piezo sensors (EPS) to monitor and analyze the condition of blended concrete systems. Moreover, the study involves blended concrete specimens are subjected to controlled damage scenarios under mechanical loadings to establish EPS as an early warning sensor for detecting damage in these systems. Furthermore, the extent of damage under these loadings is comprehensively evaluated using statistical indices and an equivalent stiffness parameter. Additionally, the study focuses on the application of machine learning (ML) algorithms to interpret EMI data for efficient damage classification. ML models, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and naive Bayes (NB) are trained to identify patterns correlating with different types and extents of structural damage. Among them, the RF classifier exhibits the highest accuracy of 0.91, followed by DT, KNN, SVM, and NB with accuracies of 0.88, 0.86, 0.68, and 0.17 respectively. The integration of EMI data with ML approaches demonstrates significant potential to revolutionize predictive maintenance strategies, enabling precise classification of structural damage and providing critical insights for engineers and stakeholders to proactively address issues, thereby significantly enhancing the safety, durability, and lifespan of concrete infrastructure. This study not only extends the current understanding of EMI data applications but also opens new paths for automated and real-time structural health monitoring.

Corrosion monitoring for transmission line based on electromechanical impedance instrumented circular piezoelectric corrosion sensor

Corrosion-induced thickness loss is a common form structural damage in transmission lines. In our previous work, a novel circular piezoelectric-metal transducer for corrosion sensing using the electromechanical impedance (EMI) technique was proposed. This transducer was fabricated by attaching a lead zirconate titanate (PZT) patch to a metal plate. In the present study, the piezoelectric-metal transducer was encapsulated and enhanced, and two types of corrosion sensors were developed: piezoelectric-steel and piezoelectric-zinc sensors. To assess the practical performance of the corrosion sensors, the piezoelectric-steel and piezoelectric-zinc sensors were connected to the angle steels and the steel strands, which are highly prone to corrosion in transmission lines. Corrosion monitoring experiments were conducted in the laboratory. The results demonstrate that the peak frequencies in the conductance signatures decrease linearly with increasing corrosion-induced thickness loss. Furthermore, it was found that piezoelectric-steel sensors are effective for quantitative monitoring of metallic corrosion, whereas piezoelectric-zinc sensors are better suited for early-stage corrosion monitoring and warning.

Research on wireless monitoring system and algorithms for preload force utilizing machine learning and electromechanical impedance

The root mean square deviation is a common way to measure the electromechanical impedance of piezoceramic transducers that are used for traditional preload monitoring. However, due to the impedance signal’s high sensitivity to temperature, most current research is carried out under the same temperature conditions to avoid its effects. Even so, it is impossible to ignore the temperature factor in practical engineering, which hinders the application of impedance technology. Therefore, this paper proposes a novel approach for preload monitoring in actual engineering: using a wireless impedance signal acquisition platform to collect long-distance impedance signals and then establishing a predicted model based on machine learning (ML) considering the temperature. The wireless impedance signal acquisition platform includes a smart washer, a piezoelectric impedance wireless sensing device, and the host computer software. This test established a dataset under various operating conditions and developed a machine-learning-based predictive model. After comparing five typical ML algorithms, it was discovered that XGBoost performed better in prediction accuracy and generalization ability. Moreover, the Shapley additive explanation method is utilized to further analyze and interpret the XGBoost model. It indicates that ML primarily relies on numerical features (such as the area of each subinterval) to identify the impedance signal and predict the prestress, whereas information features (such as temperature value, peak, etc.) have little influence on the model’s output. Finally, the results above demonstrate that the ML-based models can predict the preload at different temperatures, effectively reducing temperature interference.

Bolt looseness localization with connection-stiffness-varying flange

Electromechanical impedance analysis is a traditional method to identify the occurrence of bolt looseness, but accurate localization of blot looseness is hard to realize on the flange. In this study, a flange model with bolt connection stiffness varying with position is proposed. The location of bolt looseness can be then determined from the impedance spectrum of the model since the uniformity and symmetry of the flange are broken. The analytic model is established to reveal the distinguishability of the eigenfrequency shifting characteristics when the connection stiffness at different positions changes. The frequency shifting sequence is extracted from the coupling impedance spectrum as a feature, and the correlation between the sequences corresponding to bolt looseness at different positions is low. The relationship between the sequence and the degree of looseness is highly related so that the unknown degree of looseness can be matched with the calibrated sequence to realize the localization of the looseness. Based on the distinguishability of the frequency shifting sequence, the connection-stiffness-varying model shows great potential in the field of flanges or other connecting structures for structural health monitoring and damage localization.

Deep hybrid neural network-aided electromechanical impedance method for automated damage detection of lining concrete under freeze-thaw cycling

Deep hybrid neural network-aided electromechanical impedance method for automated damage detection of lining concrete under freeze-thaw cycling

Pattern recognition in electromechanical impedance spectroscopy damage detection of adhesive joints using multidimensional scaling

Adhesive joints are prone to various types of damage sources, which may not be identifiable with current non-destructive tests (NDTs). Structural health monitoring techniques, such as those based on electromechanical impedance spectroscopy (EMIS), aim to outperform NDTs in damage detection, by continuously monitoring structures. Although the EMIS-based algorithmic performance of damage detection has been evaluated on metallic and composite components, integrity monitoring of adhesive joints is yet to be fully determined. Therefore, this article investigates the use of multidimensional scaling (MDS) to cluster and visualize experimental impedance measurements of bonded joints in a three dimensional space. With these results, an Euclidean distance damage metric is used to try and classify the type of damage. The results show that damage detection is easily performed with the MDS algorithm, but effectiveness is dependent on the spectral measurement conditions. Furthermore, reduced dimensional spaces can yield information regarding the size and location of the damage in the adhesive layer, yielding increased knowledge on the integrity of structural adhesive joints.

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

Integrating PZT-based WP and EMI techniques with multi-criteria analysis for monitoring and assessment of mechanical properties of structural adhesives under curing

Structural adhesives are commonly employed as bonding agents in FRP strengthening systems. Measuring and assessing the mechanical properties of adhesives during the curing process is essential, as these properties directly influence the overall effectiveness of the strengthening system. This study investigated the development of mechanical properties in structural adhesives at various curing times using two PZT-based monitoring techniques, specifically wave propagation (WP) and electromechanical impedance (EMI) techniques. Two types of structural adhesives and two distinct curing temperatures (ambient temperature and mild temperature) were selected for examination. Throughout the adhesive curing process, both methods were utilized for monitoring. Subsequently, tensile tests were conducted to obtain the mechanical properties at different curing durations. The results demonstrate that monitoring indicators from both PZT-based techniques effectively captured the enhancement in adhesive performance during the curing process. Moreover, the tensile strength of the Sika and Carbon adhesives reached stability at ambient temperature after five and three days, respectively, while elevated temperatures accelerated the curing process. Furthermore, two Multi-Criteria Decision Making (MCDM) methods were employed to select monitoring indicators with the least deviation and the closest correlations with mechanical properties. These indicators have the potential to predict mechanical properties, potentially reducing the need for tensile tests.