PZT Sensors Research Articles

Crack Depth Evaluation and Imaging Using Lamb Wavefield Measurements by a Movable PZT Sensor

Crack Depth Evaluation and Imaging Using Lamb Wavefield Measurements by a Movable PZT Sensor

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.

Two-Step AE Source location for Detecting Tendon Rupture inside PC Box-Girder in-Service

In order to identify tendon rupture inside a prestressed concrete (PC) girder, new acoustic emission (AE) location system is necessary to be practically applied. In PC girder of an in-service highway, PZT sensors are installed at a web and a deck of the PC girder, and elastic waves generated by tendon rupture is simulated by steel ball impact. AE source location assuming the homogeneous velocity distribution is applied in a development view of the box-girder. Then to verify the accuracy of the location results, the effect of elastic-wave propagation at a joined corner between the web and the deck is simulated by FDM. As a result, the attenuation of first-arrival P waves was observed at the joined corner. It implies that wave energy is so attenuated that apparent velocity decreases. Finally, AE locations lead to erroneous solutions in the development view solved with a constant velocity. To solve the problem, a twostep location system is developed. In the first step, two-dimensional plane, where the tendon rupture is to be identified, is selected from the 1st and 2nd arrivals of AE Hits. Then, in the 2nd step, AE locations are determined from remaining AE sensors located on this plane. By applying this system, the location errors are within 5 % at the both web and deck in a 15 m long PC box-girder.

A Feasibility Study on EMI Techniques and Disk-Shaped PZT Sensors for Strength Monitoring of Cement-Stabilized Loess

A Feasibility Study on EMI Techniques and Disk-Shaped PZT Sensors for Strength Monitoring of Cement-Stabilized Loess

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.

Smart and autonomous monitoring of cracking in concrete structures with PZT sensor array-based hybrid sensing

Detecting microcracks in concrete structures is crucial for maintaining structural integrity; however, current methods often lack the precision, objectivity, and sensitivity needed for early-stage detection and monitoring. This study introduces a pioneering hybrid sensing approach that combines coherent and incoherent ultrasonic wave fields with surface-mounted PZT-based sensors, significantly advancing the state-of-the-art. The sensors are positioned at specified locations on a notched concrete beam and operated in the actuator-receiver (AR) mode, allowing for the manipulation of fracture width at the notch to achieve distinct degrees of damage. By evaluating the ultrasonic waves received from various AR configurations, we developed robust damage indices: the attenuation factor and diffusivity ratio. These indices provide objective metrics for evaluating the degree of damage, addressing the limitations of existing techniques. Key results demonstrate that the attenuation factor can detect crack widths as small as 10 µm at a 120 kHz frequency, surpassing traditional methods. Nevertheless, its sensitivity diminishes if the crack width exceeds 100 µm. Additionally, the diffusivity ratio remains sensitive to cracks beyond 200 µm and shows a substantial 40 % decrease even for non-visible fractures, offering a comprehensive assessment of fracture propagation. These advancements not only enhance early detection and monitoring precision but also offer a deeper understanding of fracture mechanics, paving the way for improved structural health monitoring and maintenance strategies.

Analysis of PZT Piezoelectric Sensors in Buckling Mode for Dynamic Strain Measurement

Fatigue Life Monitoring is crucial to ensure engineering structures' safety and durability. This paper introduces an innovative approach for fatigue life monitoring using a soft PZT (Lead Zirconate Titanate) piezoelectric sensor operating in buckling mode to measure applied cyclic strains. A particular emphasis is on the utilization of a 3D-printed 'Extension' that serves as the platform upon which the PZT sensor is installed and operates in buckling mode while subjected to cyclic strains. An analytical framework is presented, which encompasses the development of the governing equations to establish a direct relationship between dynamic strain values and the sensor's output. Critical strain attributes, such as amplitude and occurrence strain rate also taken into account. The analytical solutions are validated by a series of experimental tests under various dynamic loading conditions. The PZT sensor, when integrated with the 3D-printed extension and operated in buckling mode, exhibits high sensitivity and functions as a passive dynamic strain measurement sensor, with high potential to be used as self-powered sensor nodes for fatigue life monitoring of engineering structures.

Estimation of the effective range of PZT sensors for damage detection based on full wavefield measurements

Optimal location of sensors for enhanced damage detection capability is one of the key factors which needs to be considered when designing SHM system based on PZT sensors. However the sensitivity of a sensor to damage is not only dependent on its relative location with respect to elastic waves source and damage, but can also depend on structure geometry as well as particular signal features which are used for structure assessment. In the presentation, results of structure evaluation based on guided waves excited by PZT transducers and full wavefield measurements obtained by laser scanning vibrometer will be presented. The measurements were acquired for pristine state of test structure and with introduced damage, therefore it is possible to calculate spatial distribution of various signal characteristics and estimate effective range of PZT sensors for damage detection based on a given damage index. Various parameters affecting the damage detection range will be compared, in particular: type and the extent of damage, type of signal characteristic used, parameters of the excitation signal (e.g. frequency, duration), as well time interval used for damage index calculation. The obtained results may be useful for refining algorithms for sensor placement optimization as well as damage localization techniques, e.g. based on RAPID imaging algorithm.

SHM to detect and characterize impact events in metallic aircraft structure

Aircraft structures are susceptible to foreign object impacts, which may occur during manufacturing, maintenance and in-service. Sizes of these impacted objects especially during in-service can range from a small rock to a large bird. Common ways to detect such impacts are based on flight / ground crew observations and reports leading to close examinations of structures using non-destructive evaluation (NDE) techniques. If undetected these impact damages can grow during service loading and may be detrimental to flight safety. Therefore, timely detection of any signs of impact damages are critical such that proper maintenance actions can be taken. The aim is to develop methodologies using Structural Health Monitoring (SHM) techniques to detect and characterize foreign object impact events. In this experiment, a cut-out of an aluminum panel measuring 31 x 26 inches from an out-of-service aircraft was used. The panel was instrumented with four Lead Zirconate Titanate (PZT) sensors from Acellent Inc., as well as, four Acoustic Emission (AE) sensors from Mistras Inc. Impedance and susceptance measurements were acquired to assess the proper functionality of the PZT sensors before and after the impact events. Both the PZTs and the AE sensors were directly connected to a digital oscilloscope, without any amplification and / or filtering for acquiring raw data during the impact events. An instrumented multi-use tapper was designed and developed to calibrate the system, as well as, to record the impulse (impact force and time) during the impact events. The acquired data were processed using physics-based and machine learning techniques to detect and characterize the impact events.

Enhancement of PZT-based damage detection in real-scale post-tensioned anchorage under ambient conditions

Varying ambient temperature significantly affects electromechanical (EM) impedance responses of PZT sensors, leading to inaccurate damage detection results. In this study, we investigate temperature influences on the PZT's EM impedance response and introduce a filtering method to enhance PZT-based damage assessment of a real-scale post-tensioned anchorage. Firstly, the principles of the PZT-based damage identification technique are outlined. Next, the sensitivity of the PZT's EM impedance under simultaneous damage and temperature effects are extensively analyzed and quantified using finite element modelling. Thirdly, a 10-day long-term impedance monitoring of a real-scale anchorage specimen is conducted under ambient temperature conditions and different strand-looseness scenarios. The issues on damage detection performance of the PZT under varying ambient temperature are discussed. Finally, a temperature filtering technique based on principal component analysis is adopted to enhance the accuracy of strand looseness detection in the test anchorage. The numerical and experimental outcomes show that the impact of ambient temperature can overshadow the effect of strand looseness, leading to false damage diagnosis. By removing the influence of temperature on the EM impedance response, the strand looseness in the test anchorage was successfully diagnosed.

Exploring the Potential of Promising Sensor Technologies for Concrete Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for maintaining concrete infrastructure. The data collected by these sensors are processed and analyzed using various analysis tools under different loadings and exposure to external conditions. Sensor-based investigation on concrete has been carried out for technologies used for designing structural health monitoring sensors. A Sensor-Infused Structural Analysis such as interfacial bond-slip model, corroded steel bar, fiber-optic sensors, carbon black and polypropylene fiber, concrete cracks, concrete carbonation, strain transfer model, and vibrational-based monitor. The compressive strength (CS) and split tensile strength (STS) values of the analyzed material fall within a range from 26 to 36 MPa and from 2 to 3 MPa, respectively. The material being studied has a range of flexural strength (FS) and density values that fall between 4.5 and 7 MPa and between 2250 and 2550 kg/m3. The average squared difference between the predicted and actual compressive strength values was found to be 4.405. With cement ratios of 0.3, 0.4, and 0.5, the shear strength value ranged from 4.4 to 5.6 MPa. The maximum shear strength was observed for a water-cement ratio of 0.4, with 5.5 MPa, followed by a water-cement ratio of 0.3, with 5 MPa. Optimizing the water-cement ratio achieves robust concrete (at 0.50), while a lower ratio may hinder strength (at 0.30). PZT sensors and stress-wave measurements aid in the precise structural monitoring, enhanced by steel fibers and carbon black, for improved sensitivity and mechanical properties. These findings incorporate a wide range of applications, including crack detection; strain and deformation analysis; and monitoring of temperature, moisture, and corrosion. This review pioneers sensor technology for concrete monitoring (Goal 9), urban safety (Goal 11), climate resilience (Goal 13), coastal preservation (Goal 14), and habitat protection (Goal 15) of the United Nations' Sustainable Development Goals.

Experimental and numerical investigation of the bonding conditions of piezoelectric sensors under high compressive strains on structures

In the past two decades, thin lead zirconate titanate (PZT) sensors have been widely used in the electro-mechanical impedance (EMI) technique for sensing applications, particularly for monitoring civil structures. They are typically surface bonded using an industrial adhesive to the monitored structure. The bond between a PZT sensor and structure must be sufficiently strong to transmit the response of the structure to the sensor. In this study, acrylic cubes bonded with PZT patches are subjected to high compressive strains above 2000 με to develop a better understanding of bonding conditions when structures undergo such high strains. Acrylic can undergo such high strains without developing fissures or cracks. Thus, the recorded EMI response only reflects changes in the bonding condition due to the development of strains. The experiments are also numerically supplemented by simulating various debonding conditions. At higher strains, it was observed that the admittance signatures tend to behave similarly to a freely vibrating PZT patch, indicating debonding around the periphery. Even after the complete unloading of the structure, the signatures did not return to their initial state, indicating a permanent partial debonding. The strains developed on a loaded structure are not uniform and can be localized due to structural imperfections, resulting in higher strains in the region where a sensor is bonded. The insights from this study will aid in expanding the scope of the application of PZT sensors for monitoring civil structures through better comprehension of the PZT-structure bond under high compressive strains.

A Baseline‐Free Electromechanical Impedance Resonance Method for Measuring the Modulus of Elasticity of Concrete Cubes Using Surface‐Bonded PZT Patches

The modulus of elasticity of concrete (Ec) is an essential parameter commonly used in concrete design, concrete curing monitoring, and deterioration evaluation and damage detection of concrete structures. A quick and reliable in situ determination of Ec helps in accurate decision‐making for construction and maintenance. The electromechanical impedance (EMI) technique using surface‐bonded lead zirconate titanate (PZT) patches (referred to as SBP) has become a popular nondestructive method for monitoring concrete structures due to ease of operations. The existing research mainly utilized baseline‐dependent approaches to monitor the changes (e.g., hardening or damage evolution) of concrete structures. However, relevant baselines are greatly influenced by the status of PZT sensors and bonding layers, limiting the practical applications of this technique. This paper presents a baseline‐free EMI resonance method for measuring Ec of concrete without using prior baseline data for the first time. Numerical and experimental studies on standard concrete cubes (100 mm in width) with SBP were conducted to examine the proposed method. A dimensionless physical quantity was first proposed to view the EMI signals. Then, resonance peaks highly related to concrete properties but insensitive to the status of the sensor system were selected, physically described, and correlated to the concrete parameters through numerical analyses. Finally, experimental validations, covering the measurement of Ec, repeatability of the chosen resonance peaks, and temperature effects, were conducted to illustrate the proposed method’s stability, accuracy, and sensitivity.

Health monitoring of steel structures using surface mountable and detachable PZT sensor

A lot of collapse in the infrastructure were reported in the last decades, hence the need for monitoring the health of the structures also arises. There are various techniques available to monitor the health of the structure, in which Electromechanical impedance (EMI) technique is one of them. In EMI technique, the PZT sensor is bonded to/embedded in the structure using epoxy. The thickness of epoxy layer used as an adhesive should be maintained as thin as possible, which cannot be achieved ideally, which introduces shear lag effect such that is, complete strain transfer cannot take place between PZT and host structure. The shear lag effect caused is one of the major drawbacks in the EMI technique and due to this effect, the damage detection sensitivity decreases. To overcome this drawback, in this study a new sensor named Surface Mounted PZT Sensor (SMPS) is proposed for the steel structure. Along with this, the performance of other sensor configurations such as conventionally bonded PZT with epoxy that is Surface Bonded PZT Sensor (SBPS) and Adhesive Covered Surface Bonded PZT Sensor (ACSBPS) are also been compared. The results show that, SMPS was working effectively and was more sensitive compared to SBPS and ACSBPS.

Impact damage detection in woven CFRP laminates based on a local defect resonance technique with laser ultrasonics

This paper presents a local defect resonance (LDR)-based approach to detect barely visible impact damages (BVIDs) in woven CFRP laminates using laser-ultrasonic guided waves. In the experiments, broadband guided waves were excited by an Nd:YAG pulsed laser to induce resonant vibrations of BVIDs. The laser was scanned on the designed area of the woven CFRP laminates by using a galvano mirror system. A fixed broadband PZT sensor received the waves propagated from all the laser irradiation points and the datasets of the recorded waveforms were further processed to visualize the wavefield in the scanned area. A robust post-processing method was proposed to extract automatically the LDR frequencies and to obtain the image of the BVID. The feasibility of the proposed method was well validated on a woven CFRP laminate with a single BVID. In order to evaluate the detection performance, we compared our proposed method with other traditional methods, i.e., root mean square (RMS) and weighted RMS. As a result, it was found that our proposed post-processing procedure could visualize the BVID with higher contrast. Furthermore, the detection of multiple BVIDs using the developed method was demonstrated successfully.

A chemiresistive room temperature ammonia gas sensor based on self-assembled PPy/Zntpp

There is an increasing demand for monitoring ammonia in livestock farming, which can prevent livestock products from being contaminated by bacteria and viruses. As a prospective material for resistive sensors, polypyrrole (PPy) still suffers from low sensitivity and poor selectivity. Herein, zinc-tetra(p-sulfonylphenyl) porphyrin (Zntpp) particles are anchored on the PPy network by a one-step mild electrodeposition route to form a resistive sensor with the wrinkle-like nanostructure. The optimal PPy/Zntpp (Pzt) sensor demonstrates an outstanding response value of 104.3 % toward ammonia with a response/recovery time of 42/223 s, compared with that of PPy (7.2 % in response and 47/230 s). The durability and stabilities have been explored, and the limit of detection for Pzt is calculated to be ∼8.63 ppm, which enables trace ammonia in livestock farming. Additionally, the sensing mechanism can be attributed to the p-n heterojunction. Furthermore, a wireless sensor device that consists of a Pzt sensory unit, a microcomputer, and a Bluetooth module is assembled, and the concentration information can be read precisely in real-time by a smartphone, indicating the great application prospects in the field of livestock farming.

Structural Health Monitoring of Chemical Storage Tanks with Application of PZT Sensors.

Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified.

Evaluation of boundary and material influences on the dynamic electromechanical impedance response of the embedded PZT sensor in concrete

Evaluation of boundary and material influences on the dynamic electromechanical impedance response of the embedded PZT sensor in concrete

Comparative Study of Leak Detection in PVC Water Pipes Using Ceramic, Polymer, and Surface Acoustic Wave Sensors.

The detection and location of pipeline leakage can be deduced from the time arrival leak signals measured by acoustic sensors placed at the pipe. Ongoing research in this field is primarily focused on refining techniques for accurately estimating the time delays. This enhancement predominantly revolves around the application of advanced signal processing methods. Additionally, researchers are actively immersed in the utilization of machine learning approaches on vibro-acoustic data files, to determine the presence or absence of leaks. Less attention has been given to evaluating the sensitivity, performance, and overall effectiveness of these sensors in leak detection; although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due to the high attenuation of leak noise signals. The primary thrust of this research centers on identifying sensors that not only possess sensitivity but also exhibit high efficiency. To accomplish this goal, we conducted an exhaustive evaluation of the performance of three distinct categories of acoustic sensors employed for detecting water leaks in plastic pipes: specifically, lead zirconate titanate (PZT) sensors, polyvinylidene fluoride (PVDF) sensors, and surface acoustic wave (SAW) sensors. Our evaluation encompassed the performance of PVDF and SAW sensors in leak detection, comparing them to PZT sensors under a variety of conditions, including different leak sizes, flow rates, and distances from the leak. The results showed that all three sensors, when they were placed in the same position, were able to detect water leaks in plastic pipes with different sensitivities. For small leaks (1 mm, 2 mm), the PVDF sensor showed the greatest sensitivity (0.4 dB/L/h, 0.33 dB/L/h), followed by the SAW sensor (0.16 dB/L/h, 0.14 dB/L/h), and finally the PZT (0.13 dB/L/h, 0.12 dB/L/h). Similarly, for larger leaks (4 mm, 10 mm), the PVDF sensor continued to show superior sensitivity (0.2 dB/L/h, 0.17 dB/L/h), followed by the SAW sensor (0.13 dB/L/h, 0.11), and finally the PZT sensor (0.12 dB/L/h, 0.1 dB/L/h), outperforming the PZT sensor. This suggests that SAW and PVDF sensors, have the potential to serve as valuable, cost-effective alternatives to traditional commercial leak noise transducers. The outcomes of this comparative study involving three acoustic sensors hold the potential to advance the development of robust and dependable systems for the detection of water leaks in plastic pipelines.

A deep learning approach for anomaly identification in PZT sensors using point contact method

The implementation of piezoelectric sensors is degraded due to surface defects, delamination, and extreme weathering conditions, to mention a few. Hence, the sensor needs to be diagnosed before the efficacious implementation in the structural health monitoring (SHM) framework. To rescue the problem, a novel experimental method based on Coulomb coupling is utilised to visualise the evolution of elastic waves and interaction with the surface anomaly in the lead zirconate titanate (PZT) substrate. Recently, machine learning (ML) has been expeditiously becoming an essential technology for scientific computing, with several possibilities to advance the field of SHM. This study employs a deep learning-based autoencoder neural network in conjunction with image registration and peak signal-to-noise ratio (PSNR) to diagnose the surface anomaly in the PZT substrate. The autoencoder extracts the significant damage-sensitive features from the complex waveform big data. Further, it provides a nonlinear input–output model that is well suited for the non-linear interaction of the wave with the surface anomaly and boundary of the substrate. The measured time-series waveform data is provided as input into the autoencoder network. The mean absolute error (MAE) between the input and output of the deep learning model is evaluated to detect the anomaly. The MAEs are sensitive to the anomaly that lies in the PZT substrate. Further, the challenge arising from offset and distortion is addressed with ad hoc image registration technique. Finally, the localisation and quantification of the anomaly are performed by computing PSNR values. This work proposes an advanced, efficient damage detection algorithm in the scenario of big data that is ubiquitous in SHM.