Piezoelectric Wafer Active Sensors Research Articles

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.

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

Monitoring of the preload in bolted connections with piezoelectric wafer active sensors

Piezoelectric wafer active sensors are very sensitive to changes of loads in structures, which makes them useful for the load monitoring. Unfortunately, these transducers are also sensitive to environmental effects such as a change of the ambient temperature. These effects can mask the information about the load in the recorded measurement data of the piezoelectric wafer active sensors. Therefore, the effect of the load needs to be distinguished from these masking effects of the environmental influences in order to avoid misleading information about the occurring loads of the monitoring system. In this work, two monitoring procedures, which are applicable with piezoelectric wafer active sensors, shall be applied in order to have two different sources of information and be able to distinguish between the load information and the temperature influence. One approach is based on electro-mechanical impedance measurements and the other one is based on acousto-ultrasonics. The strategy of combining these two information shall be used to monitor the preload in bolted joints. In an experimental setup, piezoelectric wafer active sensors have been attached to the bolt’s head and the face side of its shaft. Then, different preload stages were set. At every load stage, acousto-ultrasonics measurements and also electro-mechanical impedance measurements were carried out. First experiments show very promising results.

Monitorization of crack propagation in welded aluminium assemblies

Utilizing welded assemblies of aluminum profiles presents an effective means of reducing mass in rolling stock. However, these lightweight and optimally stiffened structures might be susceptible to unexpected failure modes that were not accounted for during the design phase. This could be due to insufficient information about in-service conditions or uncertainties in the material and manufacturing processes. This study aims to assess the feasibility of monitoring welded aluminum assemblies in real-time under both static and cyclic loading conditions to evaluate their structural health. Mechanical tests were conducted on welded aluminum profile assemblies while employing piezoelectric wafer active sensors (PWAS) for continuous monitoring. The PWAS facilitated ultrasonic active and passive inspections during testing. Passive sensing involved utilizing the acoustic emission technique to locate the source of acoustic signals associated with damaging mechanisms like crack nucleation and propagation. Upon identifying a detrimental event, on-demand active inspections using Ultrasonic Guided Waves were deployed to examine the structure and gather crucial information about its current condition based on analyzing temporal signal features. Although our mechanical tests were conducted in a laboratory environment, we recorded some temperature variations. Hence, we compensated for environmental influences through a data fusion model. Additionally, repetitivity analyses were conducted to confirm the reliability of the proposed inspection technique. Encouragingly, results demonstrated consistency and reliability in this proposed method.

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.

Influence of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts

This study focuses on the effect of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts. We explain the influence of temperature on the basis of the phenomenological thermodynamic theory of ferroelectricity by Landau, Ginsburg and Devonshire. The article illustrates the effect of damping the radial deformation of piezoelectric sensors on the capacitance and electromechanical impedance spectra in structural health monitoring of bolts. We also explains the similarities between the effects of temperature and preload force on the electromechanical impedance spectra. We establish a clear correlation between the mechanical strain in the region of the sensor (here due to a preload force), the capacitance and the electromechanical impedance spectra and thus show that piezoelectric sensors made of lead–zirconate–titanate can be used excellently in areas of variable mechanical strain. The article enhances the understanding of the measurement method and facilitates the transfer of the measurement method to other problems in structural health monitoring. Furthermore, the acquired knowledge serves as a solid basis for verifying the plausibility of data sets containing electromechanical impedance spectra.

Temperature Hotspot Detection on Printed Circuit Boards (PCBs) Using Ultrasonic Guided Waves-A Machine Learning Approach.

This paper addresses the challenging issue of achieving high spatial resolution in temperature monitoring of printed circuit boards (PCBs) without compromising the operation of electronic components. Traditional methods involving numerous dedicated sensors such as thermocouples are often intrusive and can impact electronic functionality. To overcome this, this study explores the application of ultrasonic guided waves, specifically utilising a limited number of cost-effective and unobtrusive Piezoelectric Wafer Active Sensors (PWAS). Employing COMSOL multiphysics, wave propagation is simulated through a simplified PCB while systematically varying the temperature of both components and the board itself. Machine learning algorithms are used to identify hotspots at component positions using a minimal number of sensors. An accuracy of 97.6% is achieved with four sensors, decreasing to 88.1% when utilizing a single sensor in a pulse-echo configuration. The proposed methodology not only provides sufficient spatial resolution to identify hotspots but also offers a non-invasive and efficient solution. Such advancements are important for the future electrification of the aerospace and automotive industries in particular, as they contribute to condition-monitoring technologies that are essential for ensuring the reliability and safety of electronic systems.

Durability Assessment of Bonded Piezoelectric Wafer Active Sensors for Aircraft Health Monitoring Applications.

This study conducted experimental and numerical investigations on piezoelectric wafer active sensors (PWASs) bonded to an aluminum plate to assess the impact of bonding degradation on Lamb wave generation. Three surface-bonded PWASs were examined, including one intentionally bonded with a reduced adhesive to create a defective bond. Thermal cyclic aging was applied, monitoring through laser Doppler vibrometry (LDV) and static capacitance measurements. The PWAS with the initially defective bond exhibited the poorest performance over aging cycles, emphasizing the significance of the initial bond condition. As debonding progressed, modifications in electromechanical behavior were observed, leading to a reduction in wave amplitude and distortion of the generated wave field, challenging the validity of existing analytical modeling of wave-tuning curves for perfectly bonded PWASs. Both numerical simulations and experimental observations substantiated this finding. In conclusion, this study highlights the imperative of a high-integrity bond for the proper functioning of a guided wave-based structural health monitoring (SHM) system, emphasizing ongoing challenges in assessing SHM performance.

Real-Time Structural Damage Detection Using EMI under Varying Load a and Temperature Conditions

Structural Health Monitoring (SHM) is a critical aspect of maintaining the safety and integrity of infrastructure. In recent years, there has been a significant shift towards adopting innovative techniques, and one of the most promising methods is Electromechanical Impedance (EMI). EMI involves the utilization of piezoelectric transducers to assess the health of structures in real-time by examining changes in their electrical characteristics. The presence of load causes a change in structural stiffness, which alters the resonant characteristics of the structure. Understanding how external factors like load and temperature influence the electrical impedance of these sensors is essential for its reliable application in damage detection. This article presents an experimental and numerical study to investigate the effects of load and temperature on the electrical impedance of a piezoelectric sensor used in the electromechanical impedance (EMI) technique. The experimental setup uses an impedance analyzer (Agilent 4294A model) to measure the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The numerical model uses ANSYS software to simulate an aluminum beam at varying temperatures. The results show that the load and temperature have a significant effect on the impedance of the transducer. However, it is shown that it is still possible to detect damage using EMI even under varying load and temperature conditions. The results also show that the accuracy of EMI-based damage detection can be improved by using temperature and load compensation techniques.

AI-enabled crack-length estimation from acoustic emission signal signatures

Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.

Piezoelectric Wafer Active Sensor Transducers for Acoustic Emission Applications.

Piezoelectric materials are defined by their ability to display a charge across their surface in response to mechanical strain, making them great for use in sensing applications. Such applications include pressure sensors, medical devices, energy harvesting and structural health monitoring (SHM). SHM describes the process of using a systematic approach to identify damage in engineering infrastructure. A method of SHM that uses piezoelectric wafers connected directly to the structure has become increasingly popular. An investigation of a novel pitch-catch method of determining instrumentation quality of piezoelectric wafer active sensors (PWASs) used in SHM was conducted as well as an investigation into the effects of defects in piezoelectric sensors and sensor bonding on the sensor response. This pitch-catch method was able to verify defect-less instrumentation quality of pristinely bonded PWASs. Additionally, the pitch-catch method was compared with the electromechanical impedance method in determining defects in piezoelectric sensor instrumentation. Using the pitch-catch method, it was found that defective instrumentation resulted in decreasing amplitude of received and transmitted signals as well as changes in the frequency spectrums of the signals, such as the elimination of high frequency peaks in those with defects in the bonding layer and an increased amplitude of around 600 kHz for a broken PWAS. The electromechanical impedance method concluded that bonding layer defects increase the primary frequency peak's amplitude and cause a downward frequency shift in both the primary and secondary frequency peaks in the impedance spectrum, while a broken sensor has the primary peak amplitude reduced while shifting upward and nearly eliminating the secondary peak.

Impact energy assessment of sandwich composites using an ensemble approach boosted by deep learning and electromechanical impedance

Sandwich composites are prone to delamination and fracture during service when exposed to external low-velocity impact. One hindrance to overcome before a broader deployment of sandwich composites is the issue of impact energy assessment (IEA). To promote the solution to this issue, an ensemble deep learning approach is proposed in this study. The approach comprises data expansion, series-to-image conversion, and convolutional neural networks (CNN). The data expansion is implemented using vertical average interpolation. The enhanced data are transformed into images via the Gramian angular summation field to build an image dataset for the CNN model. To validate the developed ensemble approach, hammer-dropping impact experiments on the honeycomb sandwich composites are carried out based on the piezoelectric wafer active sensor network and electromechanical impedance measurement. Accuracy, precision, recall, and F1-score indicators are introduced to evaluate the ensemble approach performance. The above indicator values are all above 0.9600, demonstrating the effectiveness of the proposed ensemble approach in settling the IEA issue.

A rapid 2D-FDTD method for damage detection in stiffened plate using time-reversed Lamb wave

This study proposes an improved two-dimensional finite-difference time-domain method (2D-FDTD) to simulate the propagation of guided waves in three-dimensional (3D) stiffened plates, which can greatly reduce the calculation amount and accelerate the simulation process. This method can be used for damage-detection imaging based on time-reversal with wavefield reconstruction (TR-RW) as a quick numerical simulation of wavefield is available. First, the piezoelectric wafer active sensors excite and collect Lamb wave signals in the damaged stiffening plate, after which these signals are reversed in the time domain. Second, the TR signals are again triggered by each sensor in turn on the reconstructed numerical model of the tested structure with 2D-FDTD. Third, the wavefield animation is obtained through simulation. Finally, the energy of the wavefield can be focused in certain areas, indicating the location of the damage. As an example, TR-RW, combined with 2D-FDTD method, is adopted to detect damage in an integral grid-stiffened plate, showing that the proposed 2D method can decrease the calculation time from dozens of minutes in the 3D model to dozens of seconds, which is of great significance for damage-detection applications. Furthermore, compared with the virtual TR method, the combined TR-RW and 2D-FDTD method can overcome the multipath effect and improve the damage location accuracy of stiffened structures.

Ultrasonic Guided Waves for Liquid Water Localization in Fuel Cells: An Ex Situ Proof of Principle.

Water management is a key issue in the design and operation of proton exchange membrane fuel cells (PEMFCs). For an efficient and stable operation, the accumulation of liquid water inside the flow channels has to be prevented. Existing measurement methods for localizing water are limited in terms of the integration and application of measurements in operating PEMFC stacks. In this study, we present a measurement method for the localization of liquid water based on ultrasonic guided waves. Using a sparse sensing array of four piezoelectric wafer active sensors (PWAS), the measurement requires only minor changes in the PEMFC cell design. The measurement method is demonstrated with ex situ measurements for water drop localization on a single bipolar plate. The wave propagation of the guided waves and their interaction with water drops on different positions of the bipolar plate are investigated. The complex geometry of the bipolar plate leads to complex guided wave responses. Thus, physical modeling of the wave propagation and tomographic methods are not suitable for the localization of the water drops. Using machine learning methods, it is demonstrated that the position of a water drop can be obtained from the guided wave responses despite the complex geometry of the bipolar plate. Our results show standard deviations of 4.2 mm and 3.3 mm in the x and y coordinates, respectively. The measurement method shows high potential for in situ measurements in PEMFC stacks as well as for other applications that require deposit localization on geometrically complex waveguides.

A Lamb waves-based wireless power transmission system for powering IoT sensor nodes

Sensor nodes (SNs) are widely deployed for condition monitoring within closed thin-walled structures. Conventional wired power supply using cables will affect the structural integrity, and wireless power supply based on inductive coupling will be shielded by metal structures, therefore, neither is desirable. Motivated by these issues, this article presents a Lamb waves wireless power transmission (WPT) technology based on piezoelectric wafer active sensors (PWASs). A PWAS with a diameter of 7 mm was used to excite A0 single-mode Lamb waves on a 1.6 mm aluminum plate at a frequency of 150 kHz for power transmission. Two optimization strategies for the Lamb waves-based WPT system were proposed and designed, including electrical impedance matching and beamforming with a linear PWAS array. The optimization effects of these two methods were analyzed experimentally. By combining these two approaches, the maximum received power is 1.537 mW, which is 384.25 times higher than that without the optimization method. The corresponding transmission efficiency is 0.217%, which is 43.4 times higher than that without the optimization method. A power management circuit was built with a maximum output power of 1.41 mW and a corresponding conversion efficiency of 77.5%. Finally, an internet-of-things (IoT) SN is designed, and a test proves that the proposed WPT system can power IoT SNs.

Health monitoring of high-damping viscoelastic materials using sub-resonator enriched electro-mechanical impedance signatures

This article presents a recent progress of the electromechanical impedance spectroscopy methodology for monitoring high-damping viscoelastic materials (solid rocket motor propellant for instance). It employs piezoelectric wafer active sensors (PWAS) with sub-resonators which can create additional resonance peaks for enriching the diagnostic information. In order to develop an in-depth understanding of the mechanism behind the sub-resonator sensor system, an analytical investigation is conducted first. The theoretical formulation treats the sub-resonator PWAS as a mass-in-mass model, which can be seamlessly merged into the traditional 1D constrained PWAS case. The analytical solution for the impedance signature of the new sub-resonator PWAS transducer is derived. Meanwhile, parametric case studies are carried out to analyze the influence of material damping and additional mass of the sub-resonators on the impedance signature. Moreover, numerical simulations are performed to demonstrate the effectiveness of the new sensor creating additional resonance peaks. Comparative investigations are carried out with both the conventional PWAS and the sub-resonator PWAS. EMIS damage indices based on the spectral amplitude and frequency variation features are used to quantify the material degradation. Additionally, the performance of the proposed sub-resonator PWAS is demonstrated via a material creep experiment. The article finishes with summary, concluding remarks, and suggestions for future work.

Lamb Wave Based Total Focusing Method for Integral Grid-Stiffened Plate Damage Identification

The integral grid-stiffened plate is often used as the outer wall structure of spacecraft, which can reduce weight and increase strength. The Lamb wave is often used to detect damage of plate structure due to its advantages of strong penetration and minimum attenuation over long distances. When the Lamb wave crosses the stiffeners, the dispersion, reflection, transmission and mode conversion waves will be superimposed, resulting in difficulties in damage detection and structural health monitoring (SHM). This paper proposed an improved total focusing method (TFM) to locate damage in integral grid-stiffened plates based on distributed piezoelectric wafer active sensors (PWAS) array. Firstly, the appropriate working frequency was selected to generate the A0 mode Lamb wave depending on the structure and sensors, and the full matrix capture (FMC) method was introduced to collect data. Secondly, TFM was used to obtain the initial imaging results and the possible area of damage can be assessed and identified. Thirdly, the double window functions were proposed to extract the scattering signal related to the damage for the specific mode (A0). Lastly, the TFM was used again to obtain the final imaging results and the damage can be correctly identified. The proposed method has been verified by three-dimensional finite element (FE) simulation as well as experiments. The experimental results show that the damage in an aluminum alloy integral grid-stiffened plate can be located with the error of 13mm.

A baseline-free stress monitoring strategy based on acoustoelastic Lamb waves using PWAS array

In this article, the baseline-free stress monitoring methodology has been proposed. Initially, it is theoretically and experimentally proven that the impact of the external stress on the phase velocity of the Lamb waves varies with the Lamb wave propagation direction in isotropic materials. The Lamb wave propagation time difference in the specific directions can be subsequently used to calculate the existing stress. The proposed methodology can be therefore eliminated from the usage of the referential ideal echo data under zero residual stress condition, which is however the origin of the stress measurements for the existing methods. Then, a semi-analytical finite element model is established to calibrate the stress coefficient of the homogeneous prestress waveguide. The piezoelectric wafer active sensors array is finally applied to excite and receive the Lamb wave signal in the specially designed directions. Experimental verification indicates that the maximum absolute error of the stress measurement is 4.7 MPa within the external stress range from 20 to 100 MPa. It is supposed that the proposed methodology is a promising alternative for structural quality assessment and safety assurance.

An online stress monitoring strategy based on Wigner–Ville time–frequency energy extraction of single-frequency dual mode Lamb waves

In this paper, a new online uniaxial stress monitoring methodology has been developed. Based on the acoustoelastic effect of Lamb waves, multimodal information at a single frequency, in which the approximate linear fitting between group velocity ratio of two modes and stress is obtained, is fused to characterize stress. The transceiver distance can be eliminated, which is essential knowledge for existing methods, in stress coefficient calibration and practical application by using the ratio of acoustic time. The semi-analytical finite element method(SAFE) is adopted to calibrate the stress coefficient. Next, the time–energy assignment algorithm derived from the smoothed pseudo-Wigner–Ville distribution(SPWVD) is evaluated on the ideal dataset obtained by time-domain finite element(TDFE) simulation. The arrival times of different modes are extracted accurately, and the fitting results are consistent with the theoretical analysis. Finally, the laboratory experiments employing a pair of piezoelectric wafer active sensors(PWAS) are conducted to calibrate unknown coefficients. The same calibration process is carried out at three different distances, and almost the same calibration results are obtained, indicating the distance-dependent nature of the stress coefficients. According to the completed calibration, the actual measurements from the same batch show the maximum absolute error of stress measurement is 4.2MPa within the applied stress range from 20MPa to 100MPa and the standard deviation is less than 3MPa, demonstrating good measurement accuracy and consistency. It shows the great potential of the proposed strategy to measure stress at unknown propagation distances.

Improving Electromechanical Impedance Damage Detection Under Varying Temperature

The field of structural health monitoring has seen a fundamental shift in recent years, as researchers strive to replace conventional non-destructive evaluation techniques with smart material-based techniques. Perhaps the most promising of smart material techniques for developing structural health monitoring (SHM) systems is electromechanical impedance (EMI) which can be used for real-time structural damage assessment. In EMI, mechanical resonances of structure can be seen in electrical characteristics of piezoelectric transducers due to electromechanical coupling of transducer with the structure. Existence of damage will cause a structural stiffness change and therefore the resonant characteristics of the structure will be altered. This article presents an experimental and numerical study to investigate the effects of notch damage with temperature on the electrical impedance of the piezoelectric sensor used in the EMI technique. The practical implementation of the compact EMI method utilizes as its main apparatus an impedance analyser (Model Agilent 4294A) that reads the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The finite element modelling used ANSYS software three-dimensional (3D) capability to simulate an aluminium beam at varying temperatures. Real-time monitoring of the structure is achieved based on harmonic measurements. The results conclusively showed that the proposed temperature compensation technique eliminates the results ambiguity and enabled the EMI system to detect small damages that were otherwise indiscernible.