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The application of Polyvinylidene Fluoride or Polyvinylidene Difluoride (PVDF) in harvesting energy from tire deformation was investigated in this study. An instrumented tire with different sizes of PVDF-based piezoelectric patches and a tri-axial accelerometer attached to its inner liner was used for this purpose and was tested under different conditions on asphalt and concrete surfaces. The results demonstrated that on both pavement types, the generated voltage was directly proportional to the size of the harvester patches, the longitudinal velocity, and the normal load. Additionally, the generated voltage was inversely proportional to the tire inflation pressure. Moreover, the range of generated voltages was slightly higher on asphalt compared to the same testing conditions on the concrete surface. Based on the results, it was concluded that in addition to the potential role of the PVDF-based piezoelectric film in harvesting energy from tire deformation, they demonstrate great potential to be used as self-powered sensors to estimate the tire-road contact parameters. [ABSTRACT FROM AUTHOR]

Energy harvesting is a process of extracting energy from surrounding environments. The extracted energy is stored in the supply power for various applications like wearable, wireless sensor, and internet of thing (IoT) applications. The electricity generation using conventional approaches is very costly and causes more pollution in the environmental surroundings. In this manuscript, an energy-efficient, self-powered battery-less piezoelectric-based energy harvester (PE-EH) system is modeled using maximum power point tracking (MPPT) module. The MPPT is used to track the optimal voltage generated by the piezoelectric (PE) sensor and stored across the capacitor. The proposed PE system is self-operated without additional microarchitecture to harvest the Power. The experimental simulation results for the overall PE-EH systems are analyzed for different frequency ranges with variable input source vibrations. The optimal voltage storage across the storing capacitor varies from 1.12 to 1.6 V. The PE-EH system can harvest power up to 86 µW without using any voltage source and is suitable for low-power applications. The proposed PE-EH module is compared with the existing similar EH system with better improvement in harvested power. [ABSTRACT FROM AUTHOR]

Tactile sensing is increasingly attractive in various real-life applications, mainly driven by significant progress in semiconductor, sensing, and material science technologies. Different sensing techniques were suggested for specific design goals, as none of them can simultaneously feature all the properties of human skin: high spatial–temporal resolution and repeatability, ease of stretch and bend, insensitivity to environmental conditions such as electromagnetic Interference (EMI) noise and temperature variations, portability, and determining the intensity of both normal and shear stresses. This has driven researchers to suggest multimodality tactile sensors to enhance their capabilities. However, the sensing techniques focus mainly on five types: resistive, piezo-resistive, piezoelectric, capacitance, optical fiber, and visible-light cameras. This paper will reveal the most recent research on tactile sensors, including the sensor, front-end electronics, algorithms, and associated hardware accelerators. It also suggests potential design solutions that can enhance their overall performance. Thus, it can be a valuable tool for researchers and engineers involved in related research activities, as tactile sensing is essential for a wide range of applications, from robotics to medical fields and virtual/augmented reality. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) techniques, such as Electromechanical Impedance Spectroscopy (EMIS), aim to continuously monitor structures for defects, thus avoiding the need for regular maintenance. While attention has been given to the application of EMIS in the automatic detection of damage in metallic and composite components, integrity monitoring of structural adhesive joints has been comparatively neglected. This paper investigated the use of damage metrics with electrical impedance measurements to detect defects in Single-Lap Joints (SLJs) bonded with a modified epoxy adhesive. Traditional metrics using statistical and distance-based concepts, such as the Root-Mean-Squared Deviation, RMSD, or the Correlation Coefficient, CC, are addressed at detecting voids in the adhesive layer and are applied to five different spectral frequency ranges. Furthermore, new damage metrics have been developed, such as the Average Canberra Distance, ACD, which enables a reduction of possible outliers in damage detection, or the complex Root-Mean-Squared Deviation, cRMSD, which allows for the use of both the real and imaginary components of the impedance, enabling better damage detection in structural adhesive joints. Overall, damage detection is achieved, and for certain spectral conditions, differentiation between certain damage sizes, using specific metrics, such as the RMSD or cRMSD, may be possible. Overall, the RMSD or cRMSD values from damaged SLJs tend to be double the metric values from undamaged joints.

Contact localization is an essential feature of tactile sensors for biomedical, rehabilitation, surgical, and service robot applications where interaction with the environment is needed. In this work, we have developed a piezoelectric (PZT) based soft tactile sensor using the layer-by-layer fabrication technique. We placed $4\times 4$ mm2 size PZT elements in $3\times 3$ matrix form with 3 mm spaces and embedded them in silicone layers. Although the sensor has discrete brittle sensing elements, the silicone layer distributes the contact force on the surface area, which enables continuous contact localization on the sensor. Here, we studied the estimation using the collected and conditioned output voltage signals of PZT elements. First, we used a logic-based algorithm that gives results qualitatively using a developed user interface. Then we used machine learning algorithms to estimate the contact position coordinates numerically. Finally, we applied Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) algorithms and compared the results. We got our best results using a two-layer ANN algorithm which provides a mean deviation of 0.08 mm for the X-axis and 0.07 mm for the Y-axis. Besides, we had 97.20% of testing estimations with an error lower than 0.35 mm. Therefore, we believe our tactile sensor gives promising results and can be used in small-scale applications where contact localization estimation is desired.

This paper presents a new approach to the structural integration of piezoceramics into thin-walled steel components for condition-monitoring applications. The procedure for integrating the sensors into metal components is described, and their functionality is experimentally examined with a 2 mm-thick steel sheet. The signal quality of the produced sensors is investigated in a frequency range from 100 Hz to 50,000 Hz and is compared with the results of piezo patches and strain gauges under the same conditions. The results show that due to a higher signal-to-noise ratio and a better coherence, the structurally integrated piezoceramics and the piezo patches are more qualified sensors for vibration measurement in the examined frequency range than the strain gauges. The measurements also indicate that the patches provide higher amplitudes for the frequency range up to 20 kHz. Beyond that, up to 40 kHz, the integrated sensors supplied higher amplitudes. The better signal quality in different frequency ranges as well as the different manufacturing and application methods can be interpreted as an advantage or disadvantage depending on the boundary conditions of the condition-monitoring system. In summary, structural integrated piezoceramics extend the options of monitoring technology. [ABSTRACT FROM AUTHOR]

Tactile sensation is one of the most important senses in living things and smart devices to obtain the required data from the outside world, and slip is one of the main tactile information. In the present study, a slip sensor made of conductive sponge and polyvinylidene fluoride (PVDF) is proposed to sense the magnitude and direction of the sliding force. The structure and working principle of the sensor are analyzed using the finite element method. Compared with the conventional room temperature vulcanized (RTV) silicone rubber and polydimethylsiloxane (PDMS) substrates, the conductive sponge not only provides good support for PVDF film and increases its output voltage but also can be used as an electrode to transfer electric charges. The performed experiments show that the response time of the tactile unit using the conductive sponge substrate is 48 ms, and the detection range of the tactile unit is 0.1–15 N. It is found that the designed sensor has a response time of 80 ms, a recovery time of 160 ms, and a measurement error of the angle perception is 4.54° ± 1.53°. Moreover, its detection error for shear force is 8.04% ± 5.8%. Accordingly, this method can be effectively applied to distinguish the magnitude and direction of the shear force and detect the object sliding in real‐time. [ABSTRACT FROM AUTHOR]

Screen printed piezoelectric polyvinylidene fluoride–trifluoro ethylene (PVDF–TrFE)‐based sensors laminated between glass panes in the temperature range 80–110 °C are presented. No degradation of the piezoelectric signals is observed for the sensors laminated at 110 °C, despite approaching the Curie temperature of the piezoelectric material. The piezoelectric sensors, here monitoring force impact in smart glass applications, are characterized by using a calibrated impact hammer system and standardized impact situations. Stand‐alone piezoelectric sensors and piezoelectric sensors integrated on poly(methyl methacrylate) are also evaluated. The piezoelectric constants obtained from the measurements of the nonintegrated piezoelectric sensors are in good agreement with the literature. The piezoelectric sensor response is measured by using either physical electrical contacts between the piezoelectric sensors and the readout electronics, or wirelessly via both noncontact capacitive coupling and Bluetooth low‐energy radio link. The developed sensor concept is finally demonstrated in smart window prototypes, in which integrated piezoelectric sensors are used to detect break‐in attempts. Additionally, each prototype includes an electrochromic film to control the light transmittance of the window, a screen printed electrochromic display for status indications and wireless communication with an external server, and a holistic approach of hybrid printed electronic systems targeting smart multifunctional glass applications. [ABSTRACT FROM AUTHOR]

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture. [ABSTRACT FROM AUTHOR]

Statistical based reconstruction methods and signal processing tooling techniques are implemented and used to detect delaminations or debondings within composite complex items with very high precision. From the literature, it appears that although a single procedure for the estimation of structural health is a fast solution, multiple analyses based on different reconstruction methods or different damage parameters are the way to achieve maturation assessments of the methodology. This highlights the fact that the hardware and software parts of an SHM system need two different assessment and maturation ways. This work focuses on the software part by proposing a way to start assessing the outcomes. In this paper, the damage detection and localization strategy in CFRP plate-like structures with elastic guided waves excited and acquired with a circular array PWAS network is considered. Previous outcomes are compared by new analyses using a new post-processing approach based on a cross-correlation-based technique in terms of the BVID (Barely Visible Impact Damage) surface position and its center of mass. The advantage of this specific study is hopefully to enable confidence in the transition from R&D to field implementation. In addition, this work tries to evidence an improvement in terms of cost efficiency and reduced complexity while maintaining the same accuracy.

Background: Head impacts in sports can produce brain injuries. The accurate quantification of head kinematics through instrumented mouthguards (iMG) can help identify underlying brain motion during injurious impacts. The aim of the current study is to assess the validity of an iMG across a large range of linear and rotational accelerations to allow for on-field head impact monitoring. Methods: Drop tests of an instrumented helmeted anthropometric testing device (ATD) were performed across a range of impact magnitudes and locations, with iMG measures collected concurrently. ATD and iMG kinematics were also fed forward to high-fidelity brain models to predict maximal principal strain. Results: The impacts produced a wide range of head kinematics (16–171 g, 1330–10,164 rad/s2 and 11.3–41.5 rad/s) and durations (6–18 ms), representing impacts in rugby and boxing. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 for peak impact kinematics and 0.97 for predicted brain strain. We also found good agreement between iMG and ATD measured time-series kinematic data, with the highest normalized root mean squared error for rotational velocity (5.47 ± 2.61%) and the lowest for rotational acceleration (1.24 ± 0.86%). Our results confirm that the iMG can reliably measure laboratory-based head kinematics under a large range of accelerations and is suitable for future on-field validity assessments.

Geometrically nonlinear strain-based beam formulation has the potential to analyze flexible slender components in multibody systems efficiently owing to the minimum number of variables and constant stiffness matrix. The objective of this study is to develop a multibody dynamic analysis framework based on the strain-based beam formulation. To this end, we describe the constraint equation using the vector variables of the absolute nodal coordinate formulation that exhibits a velocity-transformation relationship with the strain-based formulation. Subsequently, we divide the Jacobian of the constraint equation into two terms. One term is equivalent to the velocity transformation matrix that is currently implemented in the existing strain-based analysis framework. The other term is a simple constant or linear Jacobian defined based on the orthonormal vectors of the absolute nodal coordinate formulation. This simple Jacobian description integrates the strain-based beam formulation with multibody dynamic theories to handle open- and closed-loop joints. The obtained numerical results validate that the proposed strain-based multibody dynamic analysis method concurs well with and exhibits a better convergence than the conventional flexible multibody dynamic analysis method. [ABSTRACT FROM AUTHOR]

Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device. [ABSTRACT FROM AUTHOR]

Bismuth layer–structured ferroelectric calcium bismuth niobate (CaBi2Nb2O9, CBN) is considered to be one of the most potential high‐temperature piezoelectric materials due to its high Curie temperature Tc of ∼940°C, but the drawbacks of low electrical resistivity at elevated temperature and low piezoelectric performance limit its applications as key electronic components at high temperature (HT). Herein, we report significantly enhanced dc electrical resistivity and piezoelectric properties of CBN ceramics through rare‐earth element Tb ions compositional adjustment. The nominal compositions of Ca1−xTbxBi2Nb2O9 (abbreviated as CBN‐100xTb) have been fabricated by conventional solid‐state reaction method. The composition of CBN‐3Tb exhibits a significantly enhanced dc electrical resistivity of 1.97 × 106 Ω cm at 600°C, which is larger by two orders of magnitude compared with unmodified CBN. The donor substitutions of Tb3+ ions for Ca2+ ions reduce the oxygen vacancy concentrations and increase the band‐gap energy, which is responsible for the enhancement of dc electric resistivity. The temperature‐dependent dc conduction properties reveal that the conduction is dominated by the thermally activated oxygen vacancies in the low‐temperature region (200–350°C) and by the intrinsic conduction in the HT region (350–650°C). The CBN‐3Tb also exhibits enhanced piezoelectric properties with a high piezoelectric coefficient d33 of ∼13.2 pC/N and a high Tc of ∼966°C. Moreover, the CBN‐3Tb exhibits good thermal stabilities of piezoelectric properties, remaining 97% of its room temperature value after annealing at 900°C. These properties demonstrate the great potentials of Tb‐modified CBN for high‐temperature piezoelectric applications. [ABSTRACT FROM AUTHOR]

• Piezo sensors effectively capture the dynamic strain response under simulated pedestrian motions. • Strain modal parameters are obtained with a good correlation to acceleration-based modal parameters. • Lower-frequency strain modes are obtained with higher stability than the higher-frequency strain modes. • Piezo sensors effectively capture the strain modes of pedestrian footover bridge under pedestrian traffic with excellent repeatability. This study evaluates piezo sensors for modal identification of a scaled-down prototype of a pedestrian foot-over bridge under pedestrian motions and further extends the application in real-life structures. The piezo sensors-based results are compared with the accelerometers-based displacement modal parameters. Pedestrian motions are adopted to simulate the operational conditions of a pedestrian footover bridge with different patterns of pedestrian motions. Piezo sensors are found to effectively capture the low amplitude dynamic strain response under pedestrian motions. Strain modal testing captures the modal parameters positively correlated with conventional acceleration modal testing. Staggered pedestrian motions are best suited for actuating the structure uniformly at all instrumented nodes. Piezo sensor-based modal testing is extended to modal testing of real-life footover bridges under low amplitude ambient pedestrian traffic. Piezo sensors successfully capture the bending and torsion modes with good correlation with accelerometers-based modes. The strain-based modal parameters are obtained with excellent repeatability and negligible deviation. [ABSTRACT FROM AUTHOR]

Abstract For traditional piezoelectric sensors based on poled ceramics, a low curie temperature (T c) is a fatal flaw due to the depolarization phenomenon. However, in this study, we find the low T c would be a benefit for flexible piezoelectric sensors because small alterations of force trigger large changes in polarization. BaTi0.88Sn0.12O3 (BTS) with high piezoelectric coefficient and low T c close to human body temperature is taken as an example for materials of this kind. Continuous piezoelectric BTS films were deposited on the flexible glass fiber fabrics (GFF), self-powered sensors based on the ultra-thin, superflexible, and polarization-free BTS-GFF/PVDF composite piezoelectric films are used for human motion sensing. In the low force region (1–9 N), the sensors have the outstanding performance with voltage sensitivity of 1.23 V N−1 and current sensitivity of 41.0 nA N−1. The BTS-GFF/PVDF sensors can be used to detect the tiny forces of falling water drops, finger joint motion, tiny surface deformation, and fatigue driving with high sensitivity. This work provides a new paradigm for the preparation of superflexible, highly sensitive and wearable self-powered piezoelectric sensors, and this kind of sensors will have a broad application prospect in the fields of medical rehabilitation, human motion monitoring, and intelligent robot.

According to the 2018 National Immunization Survey conducted by the Center for Disease Control and Prevention (CDC), 83.9% of the breastfeeding mothers in the United States have used a breast pump at least once. However, the majority of existing products use a vacuum-only mechanism to extract milk. This causes common breast injuries such as nipple soreness, breast-tissue damage, and lactation complications after pumping. The objective of this work was to develop a bio-inspired breast pump prototype, named as SmartLac8, that can mimic infant suckling patterns. The input vacuum pressure pattern and compression forces are inspired from term infants’ natural oral suckling dynamics captured in prior clinical experiments. Open-loop input–output data are used to perform system identification for two different pumping stages that facilitates controller design for closed-loop stability and control. A physical breast pump prototype with soft pneumatic actuators and custom piezoelectric sensors was successfully developed, calibrated, and tested in dry lab experiments. Compression and vacuum pressure dynamics were successfully coordinated to mimic the infant’s feeding mechanism. Experimental data on sucking frequency and pressure on the breast phantom were consistent with clinical findings.

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays–DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green’s functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers.

Lumbar degenerative disease (LDD) refers to the nerve compression syndrome such as neurogenic intermittent claudication and lower limb pain, which disturbs people's daily life, and its incidence increases with age. Traditional diagnosis often employs magnetic response imaging or other imaging examinations. But the radiological data have uncertain clinical correlation and often be overemphasized in clinical decision making. Here, an active‐matrix sensing array (AMSA) is proposed to measure plantar pressure during walking, in order to improve LDD diagnostic processes. An array of piezoelectric sensors with high robustness are assembled. Combined with a support vector machine (SVM) supervised learning algorithm, the system can classify the common human motions of half‐squat, squat, jump, walk and jog with an accuracy up to 99.2%, demonstrating its capability of recognizing personal activities. More importantly, in 62 clinical samples of lumbar degenerative patients, the system can perform an artificial intelligence diagnosis, achieving an accuracy of 100% with an area under receiver operating characteristic curve of 0.998, and also gives out recovery assessments after surgery. Since the personal plantar pressure is also indicative of other diseases, such as diabetes and fasciitis, the system can be extended to other medical aspects, showing a broad impact in biomedical engineering. [ABSTRACT FROM AUTHOR]

Piezoelectric transducers are used as a sensing device to study the fluids' motion. Moreover, they are used as a harvester of energy of Flow-Induced Vibration (FIV). The current FIV harvesters in the literature rely on piezoelectric cantilevers coupled with a bluff body that creates flow instabilities. This paper studies the use of piezoelectric cylinders as a novel transducer in the field of fluid mechanics, where the transducer makes use of its bluff geometry to create instability. The study was based on wind tunnel measurements performed on four piezoelectric cylinders of different sizes over a speed range of 1–7 m/s. The paper looks at the variation of the generated voltage across the Reynolds number. It also compares the spectra of the generated open-circuit voltage to the turbulence spectra features known from the literature. [ABSTRACT FROM AUTHOR]

Static and dynamic methods can be used to assess the way a foot is loaded. The research question is how the pressure on the feet would vary depending on walking/running speed. This study involved 20 healthy volunteers. Dynamic measurement of foot pressure was performed using the Ortopiezometr at normal, slow, and fast paces of walking. Obtained data underwent analysis in a "Steps" program. Based on the median, the power generated by the sensors during the entire stride period is the highest during a fast walk, whereas based on the average; a walk or slow walk prevails. During a fast walk, the difference between the mean and the median of the stride period is the smallest. Regardless of the pace of gait, the energy released per unit time does not depend on the paces of the volunteers' gaits. Conclusions: Ortopiezometr is a feasible tool for the dynamic measurement of foot pressure. For investigations on walking motions, the plantar pressure analysis system, which uses the power generated on sensors installed in the insoles of shoes, is an alternative to force or energy measurements. Regardless of the pace of the walk, the amounts of pressure applied to the foot during step are similar among healthy volunteers. [ABSTRACT FROM AUTHOR]

Partial Discharge (PD) is one of the symptoms of an electrical insulation problem, and its permanence can lead to the complete deterioration of the electrical insulation in high-voltage equipment such as power transformers. The acoustic emission (AE) method is a well-known technique used to detect and localize PD activity inside oil-filled transformers. However, the commercially available monitoring systems based on acoustic sensors still have a high cost. This paper analyses the ability of low-cost piezoelectric sensors to identify PDs within oil-filled power transformers. To this end, two types of low-cost piezoelectric sensors were fully investigated using time-domain, frequency-domain, and time-frequency analysis, separately. Thereafter, the effectiveness of these sensors for PD detection and monitoring was studied. A three-phase distribution transformer filled with oil was examined. PDs were produced inside an oil-immersed transformer by applying a high voltage over two copper electrodes, and the AE sensors were coupled to the housing of the transformer. By extracting typical features from the AE signals, the PD signals were differentiated from on-site noise and interference. The AE signals were analyzed using acoustic signal metrics such as peak value, energy criterion, and other statistical parameters. The obtained results indicated that the used low-cost piezoelectric sensors have the capability of PD monitoring within power transformers. [ABSTRACT FROM AUTHOR]

At present, piezoelectric sensors are primarily applied in health monitoring areas. They may fall off owing to the adhesive's durability, and even damage the monitored equipment. In this paper, a piezoelectric film sensor (PFS) based on a positive piezoelectric effect (PPE) is presented and a ZnO film is deposited on a GH4169 superalloy steel (GSS) substrate using magnetron sputtering. The microstructure and micrograph of ZnO piezoelectric thin films were analyzed by an X-ray diffractometer (XRD), energy dispersive spectrometer (EDS), scanning electron microscope (SEM), and atomic force microscope (AFM). The results showed that the surface morphology was dense and uniform and had a good c-axis-preferred orientation. According to the test results of five piezoelectric sensors, the average value of the longitudinal piezoelectric coefficient was 1.36 pC/N, and the average value of the static calibration sensitivity was 19.77 mV/N. We selected the sensor whose parameters are closest to the average value for the dynamic test experiment and we drew the output voltage response curve of the piezoelectric film sensor under different loads. The measurement error was 4.03% when repeating the experiment six times. The research achievements reveal the excellent performance of the piezoelectric film sensor directly deposited on a GH4169 superalloy steel substrate. This method can reduce measurement error caused by the adhesive and reduce the risk of falling off caused by the aging of the adhesive, which provides a basis for the research of smart bolts and guarantees a better application in structural health monitoring (SHM). [ABSTRACT FROM AUTHOR]

Polyoxometalates (POMs) are a class of metal oxide complexes with a large structural diversity. Effective control of the final chemical and physical properties of POMs could be provided by fine-tuning chemical modifications, such as the inclusion of other metals or non-metal ions. In addition, the nature and type of the counterion can also impact POM properties, like solubility. Besides, POMs may combine with carbon materials as graphene oxide, reduced graphene oxide or carbon nanotubes to enhance electronic conductivity, with noble metal nanoparticles to increase catalytic and functional sites, be introduced into metal-organic frameworks to increase surface area and expose more active sites, and embedded into conducting polymers. The possibility to design POMs to match properties adequate for specific sensing applications turns them into highly desirable chemicals for sensor sensitive layers. This review intends to provide an overview of POM structures used in sensors (electrochemical, optical, and piezoelectric), highlighting their main functional features. Furthermore, this review aims to summarize the reported applications of POMs in sensors for detecting and determining analytes in different matrices, many of them with biochemical and clinical relevance, along with analytical figures of merit and main virtues and problems of such devices. Special emphasis is given to the stability of POMs sensitive layers, detection limits, selectivity, the pH working range and throughput.

These mistakes include the X-ray powder diffraction patterns in Figure 1A, and several axis titles or units in axis titles of Figures 1A, 2, 7A, 9A,B, and 10A. These axis titles or units in axis titles should be in Greek letters, rather than English letters. Corrigendum to Significantly enhanced dc electrical resistivity and piezoelectric properties of Tb-modified CaBi2Nb2O9 ceramics for high-temperature piezoelectric applications The inset shows the Curie temperature Tc as a function of x content. gl We found that our manuscript titled "Significantly enhanced dc electrical resistivity and piezoelectric properties of Tb-modified CaBi SB 2 sb Nb SB 2 sb O SB 9 sb ceramics for high-temperature piezoelectric applications" (J Am Ceram Soc. GLO:6VP/01jan23:jace18749-fig-0003.jpg PHOTO (COLOR): 7 (A) Polarizations-electric field (P-E) loops and (B) electric current density-electric field (J-E) curves for the unmodified and Tb-modified calcium bismuth niobate (CBN). gl GLO:6VP/01jan23:jace18749-fig-0004.jpg PHOTO (COLOR): 9 The complex impedance spectroscopy of calcium bismuth niobate (CBN) and CBN-3 Tb measured at various temperatures: (A and B) Cole-Cole impedance plots, (C and D) frequency dependence of the imaginary part of electric modulus, and (E and F) frequency dependence of the normalized impedance Z"/Zmax"$Z^{\prime\prime}/Z {{\rm{max}}}^{^{\prime\prime}}$ and modulus M"/Mmax"$M^{\prime\prime}/M {{\rm{max}}}^{^{\prime\prime}}$. gl 2022;105:4815-26; https://doi.org/10.1111/jace.18464)1 contains several mistakes. [Extracted from the article]

Viscous dampers are a type of seismic damping equipment widely used in high-rise buildings and bridges. However, the viscosity of the damping fluid inside the viscous damper will change over time during its use, which significantly reduces the seismic performance of the viscous damper. Hence, it is necessary to monitor the viscosity of the fluid inside the damper over its service life. In this paper, a damping fluid viscosity monitoring method based on wireless impedance measurement technology is proposed. A piezoelectric sensor is installed in a damper cylinder specimen, and the viscosity of the damping fluid is determined by measuring the piezoelectric impedance value of the sensor. In this study, 10 samples of damping fluids with different viscosities are tested. In order to quantitatively correlate damping fluid viscosity and electrical impedance, a viscosity index (VI) based on the root mean square deviation (RMSD) is proposed. The experimental results show that the variation of the real part in the impedance signal can qualitatively determine the damping fluid viscosity while the proposed VI can effectively and quantitatively identify the damping fluid viscosity. [ABSTRACT FROM AUTHOR]

We present a study on wearable capacitive patches with engineered dielectrics to be used as a strain sensor and a piezoelectric sensor with multi-modal sensing capabilities. The patches consist of a parallel plate capacitive structure with highly (100%) stretchable textile electrodes and silicone or PDMS elastomers. The gauge factor of the capacitive strain sensor is enhanced two-fold with the inclusion of high- ${k}$ Barium Titanate (BTO) nanoparticles dispersed in the silicone dielectric layer without sacrificing their excellent linearity or superb durability easily exceeding 2000 cycles. In addition to enhanced dielectric response, inclusion of BTO as well as other additives, namely PZT and PVDF-TrFE, in PDMS also induces piezoelectricity in the sensors at different weight ratios systematically studied by bending and vibrational tests. Corona poling of the piezo dielectrics improves their voltage output by at least two folds. Besides the piezoelectricity, tests on unpoled samples also uncovered evidence for triboelectricity in the nanocomposites. The proposed textile patches can simultaneously detect multiple stimuli such as strain, pressure, temperature, bending, vibration, and acoustic feedback. Deployed in appropriate locations and geometries, the patches can capture critical information on the type, strength, and duration between episodic movements and environmental factors, as demonstrated via a shoe-insert, elbow tracker, respiratory and heart-rate monitors. Accordingly, capacitive textile patches offer unique capabilities for multi-modal sensing in biomedical and robotic applications as well as overall system integration that are advantageous for sensor fusion and machine learning. [ABSTRACT FROM AUTHOR]

The application of Polyvinylidene Fluoride or Polyvinylidene Difluoride (PVDF) in harvesting energy from tire deformation was investigated in this study. An instrumented tire with different sizes of PVDF-based piezoelectric patches and a tri-axial accelerometer attached to its inner liner was used for this purpose and was tested under different conditions on asphalt and concrete surfaces. The results demonstrated that on both pavement types, the generated voltage was directly proportional to the size of the harvester patches, the longitudinal velocity, and the normal load. Additionally, the generated voltage was inversely proportional to the tire inflation pressure. Moreover, the range of generated voltages was slightly higher on asphalt compared to the same testing conditions on the concrete surface. Based on the results, it was concluded that in addition to the potential role of the PVDF-based piezoelectric film in harvesting energy from tire deformation, they demonstrate great potential to be used as self-powered sensors to estimate the tire-road contact parameters.

Conical shell structures are frequently submitted to severe static and dynamic mechanical loads that can result in situations that affect the service of the systems that are part of, or even cause catastrophic failures. For this reason, a common solution is to design an active deformation control system, usually using piezoelectric patches strategically distributed along the surface of the shell structure. Moreover, these elements may be part of an energy recovery system. This paper details the methodology to topologically optimize the placement of piezoelectric elements through a characteristic function, analysing static and free vibration loading cases by means of the finite element method. Then, the optimal arrangement of the electrode with different polarization profiles is distributed throughout the entire structure. The nature of the loading cases studied corresponds to a general situation where static loads and dynamics vibration are considered. The objective function of the problem only depends linearly on the displacement fields, and therefore, the optimal electrode profile can be obtained for any combination of loads. As a consequence, this technique allows for maximising the electric charge obtained, which results in a greater capacity for monitoring, actuation and/or energy harvesting.

This paper presents the results of a long-term concrete monitoring campaign in an underground tunnel in Brussels. The system consists of several pairs of embedded ultrasonic piezoelectric transducers arranged in a pitch-catch configuration which have been placed in the concrete ceiling of the tunnel in areas where old concrete was demolished and then repaired. The monitoring system is fully automated and sends the recorded signals to a cloud-based system in our university where they are post-processed to extract indicators of structural changes in the monitored regions, and send automated email reports. A first period of six months is studied, during which the monitored areas have been repaired with skim mortar. The post-processing of the measured signals allows to identify clearly the time of repair in each zone and the evolution of the hardening process of the repair mortar. A second monitoring period of one year is then studied where it is found that despite the proposed improvement to the time stretching technique used to filter out the effects of changing environmental conditions, our indicators are still showing variations in periods when the temperature is very high in the tunnel. A method based on observed statistical correlations between the indicators computed in the different regions is then proposed and shown to be very efficient to remove the remaining variability and make the system very robust to environmental changes. Extreme value statistics is also presented as a tool to establish relevant thresholds for alarm-triggering with a very low level of potential false alarms. With all these developments, the monitoring system can automatically detect structural changes in the tunnel in real time while being robust to unavoidable changes in the environmental conditions in the tunnel. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the structural integration of piezoceramics into thin-walled steel components for condition-monitoring applications. The procedure for integrating the sensors into metal components is described, and their functionality is experimentally examined with a 2 mm-thick steel sheet. The signal quality of the produced sensors is investigated in a frequency range from 100 Hz to 50,000 Hz and is compared with the results of piezo patches and strain gauges under the same conditions. The results show that due to a higher signal-to-noise ratio and a better coherence, the structurally integrated piezoceramics and the piezo patches are more qualified sensors for vibration measurement in the examined frequency range than the strain gauges. The measurements also indicate that the patches provide higher amplitudes for the frequency range up to 20 kHz. Beyond that, up to 40 kHz, the integrated sensors supplied higher amplitudes. The better signal quality in different frequency ranges as well as the different manufacturing and application methods can be interpreted as an advantage or disadvantage depending on the boundary conditions of the condition-monitoring system. In summary, structural integrated piezoceramics extend the options of monitoring technology.

Piezoresistive or piezoelectric force sensors are widely available today. These sensors are preferred to loadcells because of their extremely reduced size, slimness, and low cost, which allow their easy inclusion in a large variety of devices including wearables. In particular, many applications are devoted to monitoring human body movements, such as those related to breathing, muscle contraction, walking, etc. However, such sensors offer variable performance, and they need to be individually calibrated and tested to ensure accurate measurements. An automated electromechanical system that allows simple mechanical tests of force sensors is proposed. The system by means of an electrical motor; a gear box; a connecting rod-crank mechanism; two pistons, and a coupling spring between them, impress sinusoidal axial forces onto the sensor under test. The system is designed as modular so that it can be customized: the force range to which the sensor is subjected, the frequency range, and the coupler with the sensor can be changed to resemble the actual application context. The actual force (read from a loadcell coupled to the sensor under test), a piston displacement, and the sensor output are simultaneously recorded. The electromechanical system generates nearly pure sinusoidal stresses at varying low frequencies (mean total harmonic distortion of 2.77%). The energy dissipated for a single stress cycle was 3.62 gf mm on average. The developed system was used to test a Force Sensitive Resistor (FSR)-based sensor and a piezoelectric (PZT) sensor. The tests revealed significant differences from the actual force values (particularly at very low frequencies), output drifts of the FSR sensor in measurements, and non-linear behaviors. The system was found to be able to provide dynamic performances, accurate calibration, and non-linear behavior of the individual sensor.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

The interruption of a three-phase induction motor (TIM) on production lines represents a high financial and operational cost. However, these machines are often exposed to mechanical and electrical failures that can cause unexpected stoppages. Among these failures, the voltage unbalance (VU) is a supply fault that can lead to winding wear, torque losses, overheating, and other side effects. In this context, the acoustic emission (AE) analysis stands out as a promising nondestructive technique (NDT) in TIM monitoring. However, the AE method was not previously completely validated for VU diagnosis, and several research gaps need to be filled. Therefore, this work proposes a novel AE approach for detection, phase identification, and magnitude classification of VU. For this purpose, an electrical machine monitored by piezoelectric sensors was subjected to different levels of unbalanced voltages. The AE signals were processed using the novel zero-cross-weighted energy (ZE) index. This metric was based on the energy of the wavelet transform (WT) coefficients weighted by zero-crossing rate values. Experimental results revealed that the proposed index proved to be effective for detecting the VU occurrence. Besides, ZE-based data separation was proposed and achieved VU phase identification. Finally, the magnitude of the unbalanced voltages was classified by linear regression. The accuracy parameters for detection, phase identification, and magnitude classification stated the reliability of the new approach. Finally, the efforts of this work provide new functionalities to traditional AE systems. [ABSTRACT FROM AUTHOR]

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• A novel piezoelectric sensor is designed for dynamic deformation monitoring of cantilever beams. • A theoretical method is proposed for reconstructing dynamic deformation from sensor signals. • A simple calibration method is presented to eliminate the need for precise sensors installation. • Dynamic deformation of a cantilever beam is effectively monitored in wind tunnel experiments. This paper proposes an effective analytical method for dynamic deformation monitoring of cantilever beams by combining piezoelectric ceramic sensors and modal analysis theory. The dynamic equation of beam with piezoelectric sensors is established using the assumed mode method, and the dynamic responses of the system are obtained. By analyzing the computational process inversely, the conversion matrix between sensor response and structural deformation is derived. The reconstruction equation for dynamic strain distribution of structure is obtained. The correctness and effectiveness of the proposed method are confirmed by comparing with the finite element method. Experiments based on this reconstruction method are designed to monitor dynamic deformation of a cantilever beam in bluff body wake in real-time. The obtained experimental results demonstrate high monitoring precision of the proposed method. This technological approach has the advantages of minimal sensor usage, cost-effectiveness, online monitoring, and potential for application in dynamic deformation monitoring of engineering structures. [ABSTRACT FROM AUTHOR]

Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device.

The work presents a structural health monitoring (SHM) electronic system with real-time acquisition and processing for the determination of impact location in laminate. The novelty of this work is the quantitative evaluation of impact location errors using the Lamb wave guided mode S0, captured and processed in real-time by up to eight piezoelectric sensors. The differential time of arrival is used to minimize an error function for the position estimation. The impact energy is correlated to the amplitudes of the antisymmetric (A0) mode and the electronic design is described to avoid saturation for signal acquisition. The same electronic system is designed to acquire symmetric (S0) low level signals by adequate gain, bandwidth, and signal-to-noise ratio. Such signals propagate into a 1.4 mm thick aluminum laminate at the group velocity of 5150 m/s with frequency components above 270 kHz, and can be discriminated from the A0 mode to calculate accurately the differential arrival time. The results show that the localization error stabilizes at a value comparable with the wavelength of the S0 mode by increasing the number of sensors up to six, and then remains constant at up to eight sensors. This suggests that a compromise can be found between sensor density and localization error. [ABSTRACT FROM AUTHOR]

Piezoelectric transducers are used as a sensing device to study the fluids’ motion. Moreover, they are used as a harvester of energy of Flow-Induced Vibration (FIV). The current FIV harvesters in the literature rely on piezoelectric cantilevers coupled with a bluff body that creates flow instabilities. This paper studies the use of piezoelectric cylinders as a novel transducer in the field of fluid mechanics, where the transducer makes use of its bluff geometry to create instability. The study was based on wind tunnel measurements performed on four piezoelectric cylinders of different sizes over a speed range of 1–7 m/s. The paper looks at the variation of the generated voltage across the Reynolds number. It also compares the spectra of the generated open-circuit voltage to the turbulence spectra features known from the literature.

Static and dynamic methods can be used to assess the way a foot is loaded. The research question is how the pressure on the feet would vary depending on walking/running speed. This study involved 20 healthy volunteers. Dynamic measurement of foot pressure was performed using the Ortopiezometr at normal, slow, and fast paces of walking. Obtained data underwent analysis in a “Steps” program. Based on the median, the power generated by the sensors during the entire stride period is the highest during a fast walk, whereas based on the average; a walk or slow walk prevails. During a fast walk, the difference between the mean and the median of the stride period is the smallest. Regardless of the pace of gait, the energy released per unit time does not depend on the paces of the volunteers’ gaits. Conclusions: Ortopiezometr is a feasible tool for the dynamic measurement of foot pressure. For investigations on walking motions, the plantar pressure analysis system, which uses the power generated on sensors installed in the insoles of shoes, is an alternative to force or energy measurements. Regardless of the pace of the walk, the amounts of pressure applied to the foot during step are similar among healthy volunteers.