Piezoelectric Acoustic Emission Sensors Research Articles

A Review on Acoustic Emission Testing for Structural Health Monitoring of Polymer-Based Composites.

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as low as 28 µm-PVDF) allows embedding the sensors within the laminated composite, creating a smart material. Incorporating piezoelectric sensors within composites has several benefits but presents numerous difficulties and challenges. This paper provides an overview of acoustic emission testing, concluding with a discussion on embedding piezoelectric AE sensors within fibre-polymer composites. Various aspects are covered, including the underlying AE principles in fibre-based composites, factors that influence the reliability and accuracy of AE measurements, methods to artificially induce acoustic emission, and the correlation between AE events and damage in polymer composites.

Investigation of Acoustic Emission Wave Propagation Pattern with Defect Locations in a Multilayer Structure

Acoustic emission (AE) is used to monitor conditions of various structures across many industrial sectors, including containment vessel or storage tank of nuclear materials. Periodic monitoring, inspection, and analysis of structure conditions can help prevent failure and accidents. Understanding the transient elastic waves in multi-layered structures (planar or rounded types) has long been of great interest. This paper experimentally investigates changes in AE wave propagation patterns in multilayer planar structures (detecting and assessing the effect of coating layers, assumed surrogate of deposits or protective layer). Epoxy phenolic coated two mild steel plates were assembled (without any adhesion), and two piezoelectric AE sensors were placed on the coating layer. The pencil lead break (PLB) test was used to initiate the AE waves from the surface and cross-section of different layers. From wavelet transforms (WT) analysis, significant energy zone changes were observed up to the 450 kHz frequency level with PLB on the surface and cross-section of different layers. Love wave propagation on the coated plate structure resulted in wave pattern changes with PLB locations and layers. Wave duration, energy, energy ratio, and peak amplitude levels were also analysed to characterise the AE wave pattern relationship with defect location in a multilayer plate-like (planar) structure. The approaches used in this work could potentially be useful in providing a greater understanding of defects within multilayer nuclear containment structures, and also offering an alternative way to monitor corrosion related degradation of structures with insulations.

Preparation and acoustic properties of high-temperature acoustic emission sensor based on La3Ga5SiO14 crystal

With the rapid development of modern industries, the high-temperature piezoelectric sensors that can work in extreme environments are in great demand. In this work, langasite (La3Ga5SiO[Formula: see text], LGS), as a high-temperature piezoelectric crystal with stable electro-elastic performance, is used as core element, and air and porous Al2O3 are selected as backing layers respectively to prepare two kinds of high-temperature acoustic emission (AE) sensors. The detection sensitivities at 25-500[Formula: see text]C are analyzed by the ball falling test and Hsu–Nielsen experiment. Under the condition of 25–500[Formula: see text]C, the received amplitude signals by both sensors are maintained above 90 dB stimulated by the ZrO2 ceramic ball dropping. In the Hsu–Nielsen experiment, as the temperature rising from 25[Formula: see text]C to 500[Formula: see text]C, the signal amplitude of sensor with air backing layer decays from 447 mV to 365 mV, while the signal amplitude varies from 270 mV to 203 mV for the sensor with porous Al2O3 backing layer. Significantly, compared with the bandwidth of the air-backing sensor (37–183 kHz), the sensor with porous Al2O3 backing layer broadens bandwidth to 28–273 kHz. These results show that both these AE sensors have strong and stable response ability to AE signals at high-temperature of 500[Formula: see text]C. Therefore, piezoelectric AE sensor based on LGS has great potential application in the field of high-temperature structural health monitoring.

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

Acoustic emission (AE) technology is suitable for monitoring the status of high-speed train bearings owing to its high sensitivity and real-time dynamic monitoring capabilities. However, a complete theoretical model of the AE sensor, which is the core component of AE signal sensing equipment, has not yet been reported. The existing matching layer models do not account for wave attenuation in the matching layer. To address these shortcomings, we established a novel piezoelectric AE sensor design and modeling method. First, a rough contact model is established for piezoelectric ceramics, and the influence of roughness on size selection of piezoelectric ceramics is analyzed. Second, the sound intensity transmission coefficient (SITC) model of the matching layer is established considering the attenuation of AE waves, and the corresponding relationship between the attenuation coefficient and the optimum thickness of the matching layer is derived. Then a complete finite element (FE) model of an AE sensor is established, and the electroacoustic properties of the AE sensor are numerically simulated based on acoustic and piezoelectric coupling. Furthermore, AE sensors with different thicknesses of matching layers are constructed, and the validity of the mathematical model of the matching layer is verified through a lead-breaking experiment. Thereafter, a novel comprehensive performance evaluation index (CPEI) is designed through principal component analysis (PCA) based on hit parameters of AE sensors. Finally, the effectiveness and environmental adaptability of the AE sensor is verified by performance testing under complex conditions near an actual high-speed train line. The proposed method can provide a valuable theoretical framework for AE sensor design and status monitoring of high-speed train bearings.

Flexible Pb(Zr0.52,Ti0.48)O3 thin film acoustic emission sensor for monitoring partial discharge in power cable

Piezoelectric acoustic emission sensors can be used detect the sound emitted by the target structure when it is damaged and have important applications in the field of structure health monitoring. However, due to the mismatch of the interface acoustic impedance, it is hard for the conventional ultrasonic sensor to monitor the acoustic emission in a pipe structure. In this work, a flexible sensor by the deposition of a Pb(Zr0.52,Ti0.48)O3 thin film on a mica substrate was fabricated, and the acoustic emission generated by the partial discharge of a 110 kV power cable was detected by using the flexible sensor. The flexible sensor was designed with an electromagnetic shielding structure and, therefore, can screen most of the electromagnetic interference. The flexible sensor shows a relatively flat response in the frequency range from 100 to 1000 kHz with a sensitivity over 47.5 dB, which is beneficial for pattern recognition studies of acoustic emission. This work not only provides a flexible, anti-electromagnetic interference and broadband sensor for acoustic emission detection but also promotes the development and application of flexible ferroelectric materials.

A multifunctional rock testing system for rock failure analysis under different stress states: Development and application

The stress state in a rock mass is complex. Stress redistribution around underground excavation may lead to various failure modes, including compressive-shear, tensile-shear, and tensile failures. The ability to perform laboratory tests with these complex stress states is significant for establishing new strength criteria. The present paper introduces a new rock testing system with “tensile-compressive-shear” loading functions. The device includes bi-directional and double-range hydraulic cylinders, auxiliary loading equipment, and roller rows that can perform direct compressive-shear tests, direct tensile tests, and direct tensile-shear tests. The testing system provides maximum vertical and lateral loading forces of 2000 kN and allows testing cubical rock specimens with dimensions of 0.5 m × 0.5 m × 0.5 m. The performance of the testing machine was evaluated by testing a rock-like material based on cement mortar under compressive-shear, tensile, and tensile-shear stress states. The failure process and deformation characteristics were monitored during loading using acoustic emission (AE) transient recorder, piezoelectric AE sensors, a high-speed camera, and a thermal infrared camera. The failure mechanism was investigated by analyzing AE counts, AE amplitude, strain, and temperature changes on the rock specimen surface. The test results confirmed that the testing system could successfully simulate the abovementioned stress path. The AE counts and amplitude responses were influenced by different failure modes. The temperature response during the compressive-shear test indicated the development of a high-temperature band on the rock specimen surface. In contrast, a negligible temperature change was observed during the tensile and tensile-shear tests. The newly developed multifunctional rock testing system allows laboratory tests under various failure modes. The monitoring results of multiple variables during rock failure tests provide valuable information on failure characteristics. • Develop a novel rock testing system with tensile-compressive-shear loading function. • Investigate the failure behavior of rock-like specimens under different stress states. • Analyze the various attributes response characteristics in different failure modes.

Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel

Structural health monitoring for lightweight complex composite structures is being investigated in this paper with a data-driven deep learning approach to facilitate automated learning of the map of transformed signal features to damage classes. Towards this, a series of acoustic emission (AE) based laboratory experiments have been carried out on a composite sample using a piezoelectric AE sensor network. The registered time-domain AE signals from the assigned sensor networks on the composite panel are processed with the continuous wavelet transform to extract time-frequency scalograms . A convolutional neural network based deep learning architecture is proposed to automatically extract the discrete damage features from the scalogram images and use them to classify damage-source regions in the composite panel. The proposed deep-learning approach has shown an effective damage monitoring potential with high training, validation and test accuracy for unseen datasets as well as for entirely new neighboring damage datasets. Further, the proposed network is trained, validated and tested only for the peak-signal data extracted from the raw AE data. The application of peak-signal scalogram data has shown a significant improvement in damage-source classification performance with high training, validation and test accuracy.

Study of the Use of Piezoelectric Diaphragm as a Low-Cost Alternative to the Acoustic Emission Sensor in Dressing Operation of Aluminum Oxide Wheels

Acoustic emission (AE) sensors have been widely used for tool condition monitoring in the grinding process. Recently, the piezoelectric diaphragm (piezoelectric buzzer) was introduced as a low-cost alternative for the same purpose. In this context, the present research work aims to study the efficiency of the piezoelectric diaphragm as a viable substitute to AE sensors in monitoring the dressing of structurally distinct aluminum oxide grinding wheels. Dressing tests were conducted using two industrial wheels, which were subjected to various dressing conditions. The signals generated by the AE and piezoelectric diaphragm sensors were collected simultaneously and subsequently analyzed in both time and frequency domains. The most relevant features related to the dressing were extracted through the following metrics: energy criterion, Pearson correlation coefficient, power spectral density (PSD), fast Fourier transform (FFT), and magnitude-squared coherence (MSC). Correlation greater than 80% was found between the piezoelectric diaphragm and AE sensor signals for determining the actual grinding wheel condition, which indicates the piezoelectric diaphragm as a viable alternative to the commercial AE sensors in dressing operation monitoring.

Effects of periodic cavitation on steam–water flow regime transition and mixing near steam nozzle exit

Abstract Supersonic steam injection from underwater vehicles into surrounding bulk water exhibits the formation of coherent structures due to the interfacial interaction between the steam and water. The mixing between the two is a function of the rate of growth of shear layer. In present work, experimental study is conducted with minor contribution from the CFD, to highlight the phenomena associated to the high-pressure steam injection into a pool of water under the influence of periodic cavitation which occurs near the steam's nozzle exit with its opening being at right angle to the opening of the exit nozzle. PIV setup along with piezoelectric acoustic emission sensors as well as LM35 temperature sensors and pitot tubes were applied to characterize the growth of the shear layer as a function of periodic cavitation with a range of steam's operating pressure. Based on the normalized shear growth rate as well as the Strouhal number and the normalized pitot thickness, the effect of rising in the cavitation on the variations of the thickness of the shear layer was studied. It was observed that higher area under the influence of the shear layer was due to the domination of the coherent flow structures, which influenced improved mixing between the steam and water. Comparison of our data with the available shear growth rate in literature shows good agreement when compared as a function of Mach number.

All-Fiber Laser-Self-Mixing Sensor for Acoustic Emission Measurement

Self-mixing interferometry (SMI) is a promising non-destructive sensing technology with advantages of simplicity in system structure, low cost in implementation, ease in optical alignment, and high resolution in measurement. It consists of a laser diode, a photodiode packaged at the rear of the laser diode, a lens, and a target to be measured. Typically, SMI-based sensing is to measure the phase change in an SMI signal with a fringe pattern as sinusoidal or saw-tooth like. The phase is called optical phase in external cavity that is linked to the quantity to be measured, e.g., displacement, velocity, and vibration. Each SMI fringe maps to a half-laser-wavelength displacement of the target. With increasing feedback level in an SMI system, some fringes may lost or even totally disappear. In this case, the laser output from an SMI system can trace the optical phase to achieve a linear sensing relationship. In this article, we design an all-fiber SMI linear sensor for real-time and fast tracking acoustic emission (AE) signals. Firstly, a system design in terms of selection of operating parameters is presented. Then, a proof of concept experimental system was built for detecting AE signals. The results show that the proposed SMI sensor has comparable AE detection capability to piezoelectric sensor, providing a compact and cost-effective optical AE sensing solution without need of extra signal processing, which can be considered as a viable alternative to the well-established piezoelectric AE sensor.

Monitoring of the High-Technology Nailing of CFRTP Material under Ultrasonic Vibration by Acoustic Emission Method

In this study, the potential to monitor the high-technology nailing of carbon fiber reinforced thermoplastic material (CFRTP) under ultrasonic vibration was investigated by acoustic emission (AE) method. AE signals were detected by a piezoelectric AE sensor during high-technology nailing under ultrasonic vibration. This paper describes some experimental results on AE signal characteristics and observation of the high-technology nailing. In order to investigate the effects of machining condition, we focused on RMS voltage, which is dependent on the energy parameter of the AE signal. It was found that the AE method is a useful method of monitoring high-technology nailing.

Multichannel fiber laser acoustic emission sensor system for crack detection and location in accelerated fatigue testing of aluminum panels

Detection and location of the source of acoustic emission in a thin aluminum panel is demonstrated using a multichannel fiber laser sensor system. Acoustic emission generated by a crack in an aluminum panel used as a test coupon in an accelerated fatigue experiment is detected and the location of the crack identified. Acoustic emission is detected over a bandwidth of around 0.5 MHz from a serially multiplexed array of four laser sensors and compared with measurements taken from four piezo-electric sensors co-located with the fiber laser sensors. The location of the crack is determined by first estimating time difference of arrival of signals at each sensor using a novel algorithm based on the cumulative distribution transform method with hyperbolic positioning. The fiber laser sensor is shown to match the signal-to-noise ratio of the industry standard (Mistras S9225) piezo-electric acoustic emission sensor.

Development of a 25kN <i>In Situ</i> Load Stage Combining X-Ray Computed Tomography and Acoustic Emission Measurement

Combination of material testing methods such as X-ray computed tomography with in-situ load stages allows for detailed analysis of damage formation and progression in fibre-reinforced composites. X-ray computed tomography is highly suited to volumetrically analyse the damage evolution induced by the load stage for tensile testing after subsequent load increments. Simultaneous acoustic emission monitoring allows identifying the occurrence of particular failure mechanisms and allows stopping the loading procedure for volumetric scanning. However, typical commercial designs focus on a broad range of materials and are not necessarily optimized for high load capacity at high voxel resolution or the possibility to attach acoustic emission sensors to the test sample. Accordingly, we designed a new load stage to fit larger samples up to 180 mm in length and 18 mm in width, which also allows two piezoelectric acoustic emission sensors to be directly applied on the sample. In order to test fibre reinforced laminate samples with a relevant cross-section, the support structure of the load stage is made of a carbon fibre reinforced polymer tube, which withstands a maximum load of 25 kN and still stays reasonably X-ray transparent. With an outer diameter of 27 mm, a computed tomography scan with a resolution down to 2.6 μm is still possible for these laminate cross-sections. This allows to study in detail how matrix and fibres behave under loads in laminates, which are comparable to specimen sizes by typical test standards. As example, we present results from glass fibre-reinforced epoxy samples with a [±45°]5layup and carbon fibre-reinforced epoxy samples with a [0,90,90,0] layup.

Piezoelectric MEMS acoustic emission sensors

In this paper, piezoelectric MEMS (piezo-MEMS) sensors are designed and characterized for detecting elastic waves released by active flaws, known as acoustic emission (AE). The designed sensors operate in plate flexural mode or rigid body mode driven by the vibration of silicon diaphragm where piezoelectric layer is deposited on. The designed device contains two different frequency sensors tuned to 40 kHz and 200 kHz. The microstructural layers consist of doped silicon, aluminum nitride (AlN) and metal layer, which function as the bottom electrode, sensing layer and top electrode, respectively. The silicon layer also functions as a mass-spring system where a movable plate is connected with four partially clamped beams to the substrate. As the geometry sizes are restricted by the design rules of the manufacturing process, an additional mass is provided for the 40 kHz sensor through the substrate etching process. The sensors are numerically modeled using COMSOL Multiphysics software to obtain the frequency response and the dynamic response under excitation. Afterwards, the sensors are manufactured using Piezoelectric Multi-User MEMS Process (PiezoMUMPs) provided by MEMSCAP foundry. The performance of the designed sensors is demonstrated through electromechanical characterization experiments. A face-to-face response is obtained for each sensor, and the performance of the designed sensors is compared with the conventional piezoelectric AE sensors. The developed piezo-MEMS AE sensors have advantages of small foot-print, low cost and no need for bias voltage for operation.

Detection of Ultrasonic Stress Waves in Structures Using 3D Shaped Optic Fiber Based on a Mach-Zehnder Interferometer.

This work proposes a 3D shaped optic fiber sensor for ultrasonic stress waves detection based on the principle of a Mach–Zehnder interferometer. This sensor can be used to receive acoustic emission signals in the passive damage detection methods and other types of ultrasonic signals propagating in the active damage detection methods, such as guided wave-based methods. The sensitivity of an ultrasonic fiber sensor based on the Mach–Zehnder interferometer mainly depends on the length of the sensing optical fiber; therefore, the proposed sensor achieves the maximum possible sensitivity by wrapping an optical fiber on a hollow cylinder with a base. The deformation of the optical fiber is produced by the displacement field of guided waves in the hollow cylinder. The sensor was first analyzed using the finite element method, which demonstrated its basic sensing capacity, and the simulation signals have the same characteristics in the frequency domain as the excitation signal. Subsequently, the primary investigations were conducted via a series of experiments. The sensor was used to detect guided wave signals excited by a piezoelectric wafer in an aluminum plate, and subsequently it was tested on a reinforced concrete beam, which produced acoustic emission signals via impact loading and crack extension when it was loaded to failure. The signals obtained from a piezoelectric acoustic emission sensor were used for comparison, and the results indicated that the proposed 3D fiber optic sensor can detect ultrasonic signals in the specific frequency response range.

Novel Fiber-Optic Ring Acoustic Emission Sensor.

Acoustic emission technology has been applied to many fields for many years. However, the conventional piezoelectric acoustic emission sensors cannot be used in extreme environments, such as those with heavy electromagnetic interference, high pressure, or strong corrosion. In this paper, a novel fiber-optic ring acoustic emission sensor is proposed. The sensor exhibits high sensitivity, anti-electromagnetic interference, and corrosion resistance. First, the principle of a novel fiber-optic ring sensor is introduced. Different from piezoelectric and other fiber acoustic emission sensors, this novel sensor includes both a sensing skeleton and a sensing fiber. Second, a heterodyne interferometric demodulating method is presented. In addition, a fiber-optic ring sensor acoustic emission system is built based on this method. Finally, fiber-optic ring acoustic emission experiments are performed. The novel fiber-optic ring sensor is glued onto the surface of an aluminum plate. The 150 kHz standard continuous sinusoidal signals and broken lead signals are successfully detected by the novel fiber-optic ring acoustic emission sensor. In addition, comparison to the piezoelectric acoustic emission sensor is performed, which shows the availability and reliability of the novel fiber-optic ring acoustic emission sensor. In the future, this novel fiber-optic ring acoustic emission sensor will provide a new route to acoustic emission detection in harsh environments.

Numerical approach to absolute calibration of piezoelectric acoustic emission sensors using multiphysics simulations

In this paper, the absolute calibration of piezoelectric Acoustic Emission (AE) sensors is developed using multi-physics numerical and experimental models. In this process, two common excitation approaches including laser and pencil lead break (PLB) are used to generate the simulation sources because of their excellent reproducibility and well-established analytical expressions of their source functions. The numerical models include source function, steel plate and piezoelectric sensor model with the details including adhesive layer, wear plate, piezoelectric ceramic, and backing material. The experimental results of four types of AE sensors (true wideband sensor, low and high frequency conventional sensors and low frequency unpackaged sensor) are compared with the numerical results. The numerically obtained calibration curves show good agreement with the calibration curves provided by the manufacturer. The influences of adhesive layer, backing material and sensor size on the sensor response are studied. The uncoupled piezoelectric sensor models indicate that there is no single displacement history that the sensors can be used for calibration. It is shown that the quantitative AE analysis requires the coupled structural and electrical models of structural medium and piezoelectric sensor.

Potential of acoustic emissions from three point bending tests as rock failure precursors

Development of failure in brittle materials is associated with microcracks, which release energy in the form of elastic waves called acoustic emissions. This paper presents results from acoustic emission measurements obtained during three point bending tests on Nestos marble under laboratory conditions. Acoustic emission activity was monitored using piezoelectric acoustic emission sensors, and the potential for accurate prediction of rock damage based on acoustic emission data was investigated. Damage localization was determined based on acoustic emissions generated from the critically stressed region as scattered events at stresses below and close to the strength of the material.

Evaluation of damage progression in concrete structures due to reinforcing steel corrosion using acoustic emission monitoring

Acoustic emission (AE) monitoring was employed to detect and identify the extent of degradation resulting from reinforcing steel corrosion in concrete structures. Reinforced concrete samples were subjected to an accelerated corrosion test and corroded until they reached five different levels of mass loss of steel: 1, 2, 3, 4, and 5 %. The corrosion activity of all samples was continuously monitored using attached piezoelectric AE sensors and a multichannel data acquisition system. The results obtained from AE monitoring were analysed and compared to those obtained from half-cell potential (HCP) measurements, the amount of anodic current passing with time, and concrete cover cracking. The study analysed AE signal strength parameters including cumulative signal strength, historic index [H(t)], and severity (Sr) to detect the corrosion initiation, and subsequently, to classify the extent of damage following the corrosion progression. The study also aimed to correlate these parameters to both the degree of mass loss and concrete cover crack width. The results showed that the AE signal strength parameters can be exploited to characterize different degrees of damage from corrosion initiation and up to 5 % steel mass loss. These parameters can be analysed to detect the corrosion initiation earlier than the HCP test. This study also presented damage classification charts that can be used to correlate the level of steel mass loss and/or crack width to the AE signal strength parameters. Given these points, the outcomes of this investigation are substantial for advancing the application of acoustic emission for structural health monitoring in concrete structures.

Detection of Damaged Tool in Injection Molding Process with Acoustic Emission

ABSTRACTThe measurement of acoustic emission (AE) signals during injection molding of polypropylene with new and damaged mold is presented. The damaged injection mold was fitted with a steel insert with cracks induced by laser surface heat treatment. Two resonant piezoelectric AE sensors were attached to the mold via AE waveguides. To improve the mold integrity prediction with smaller defects, AE signal frequency characteristics and a measure of AE signal amplitude probability distribution are implemented. A 5-dimensional feature vector with real-valued explanatory variables is proposed, providing the defining points in an appropriate multidimensional space to characterize the state of injection molding tool. Feature vectors are classified with neural network pattern recognition. The results confirm that presented AE technique offers characterizing the integrity of molds also with resonant sensors.