Structural Health Monitoring Applications Research Articles

STRAIN TRANSFER ANALYSIS OF A FOUR-LAYER EMBEDDED FIBER OPTIC SENSING MODEL IN AN ELASTIC STATE

Optical fiber grating strain sensors are currently utilized in a variety of structural health monitoring applications. The encapsulated fiber optic sensor is unable to completely detect the strain of the structure, so the strain transfer theory should be established to maximize the strain sensing of fiber. It is required to explore the embedded four-layer fiber optic sensing model to create a more plausible strain transfer error hypothesis. Based on the three-layer fiber optic sensing model, the Goodman assumption and Fourier series approach were presented to study the strain transfer efficiency of the four-layer model in the elastic state. First, the physical quantity to be analyzed is determined, and finally the radius, interlayer bonding coefficient and elastic modulus are selected as the parameters affecting the strain transfer efficiency. The length range of efficiency evaluation is 0–2.5[Formula: see text]m, and the transfer efficiency under different radii is above 0.90 when the length [Formula: see text][Formula: see text]m. The interlayer bonding coefficient [Formula: see text] between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 has little impact on the transfer efficiency, and the same is true for [Formula: see text], so it cannot be considered in practice. When [Formula: see text] is between [Formula: see text][Formula: see text]N/m3 and [Formula: see text][Formula: see text]N/m3 and the length [Formula: see text][Formula: see text]m, the strain transfer coefficient reaches 95%. The influence of elastic modulus on the transfer efficiency is very significant when [Formula: see text][Formula: see text]m. The four-layer model performs similarly to the three-layer model within the paste length range of 1.0[Formula: see text]m, but has a superior strain transfer effect when the pasted length exceeds 1.0[Formula: see text]m. As the radius of the protective layer rises, the effect of strain transfer deteriorates.

A0 mode Lamb wave propagation in a nonlinear medium and enhancement by topologically designed metasurfaces for material degradation monitoring

This paper intends to provide an application example of using metamaterials for elastic wave manipulation inside a nonlinear waveguide. The concept of phase-gradient metasurfaces, in the form of artificially architectured structures/materials, is adopted in nonlinear-guided-wave-based structural health monitoring (SHM) systems. Specifically, the second harmonic lowest-order antisymmetric Lamb waves (2nd A0 waves), generated by the mutual interaction between primary symmetric (S) mode and antisymmetric (A) mode waves, show great promise for local incipient damage monitoring. However, the mixing strength is adversely affected by the wave beam divergence, which compromises the 2nd A0 wave generation, especially in the far field. To tackle this problem, a metasurface is designed to tactically enhance the 2nd A0 waves through manipulating the phases and amplitudes of both primary waves simultaneously. After theoretically revealing the features of the 2nd A0 wave generation in a weakly nonlinear plate, an inverse-design strategy based on topology optimization is employed to tailor-make the phase gradient while ensuring the high transmission of the primary waves, thus converting the diverging cylindrical waves into quasi-plane waves. The efficacy of the design is tested in a 2nd-A0-wave-based SHM system for material degradation monitoring. Results confirm that the manipulated S and A mode waves can propagate in a quasi-planar waveform after passing the surface-mounted metasurface. Changes in material properties inside a local region of the host plate can be sensitively captured through examining the variation of the 2nd A0 wave amplitude. The concept presented here not only showcases the potential of metamaterial-enhanced 2nd A0 waves for material degradation monitoring, but also illuminates the promising direction of metamaterial-aided SHM applications in nonlinear waveguides.

Application of Long-Period Fiber Grating Sensors in Structural Health Monitoring: A Review

Structural health monitoring (SHM) is crucial for preventing and detecting corrosion, leaks, and other risks in reinforced concrete (RC) structures, ensuring environmental safety and structural integrity. Optical fiber sensors (OFS), particularly long-period fiber gratings (LPFG), have emerged as a promising method for SHM. Various LPFG sensors have been widely used in SHM due to their high sensitivity, durability, immunity to electromagnetic interference (EMI) and compact size. This review explores recent advancements in LPFG sensors and offers insights into their potential applications in SHM.

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.

Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.

Advancement of data-driven SHM: A research paradigm on AE-based switch rail condition monitoring

The past ten years have witnessed the tremendous progress of structural health monitoring applications in civil infrastructures. This is particularly embodied in railway engineering. The increasing train speed brings greater challenges to safety and ride comfort, and the primary theme of maintenance has been gradually altered from offline inspection to online monitoring. Rail operators must get an in-time warning of potential structural defects before critical failure takes place. It is more favourable that the rail operators can take hold of the real-time status of the key components and infrastructures in railway systems. This paper summarizes a long-term research series by the authors’ research team on online monitoring of rail tracks at turnout areas utilizing acoustic emission-based sensing technique, and more importantly, successively advancing signal processing methods and data-driven analysing frameworks, covering Bayesian inference, convolutional neural networks, transfer learning and task similarity analysis. The proposed algorithms tackle noise interference brought by wheel-rail impacts, great uncertainties in an open environment, and insufficiency of monitoring data, and realize comprehensive monitoring of rail tracks in turnout areas from basic crack detection to regressive condition assessment step-by-step.

Smartphone application for structural health monitoring of stay cables: Edgar Cardoso Bridge

App4SHM is a mobile system for structural health monitoring of bridges. It consists of a front-end smartphone application, available on both Android and iOS, that is used to measure the vibration frequencies and a backstage server software, accessible through any internet browser, used to perform most of the calculations and to manage the measurement databases. This paper focuses on the App4SHM module for cables, where the frequency observations are directly converted into cable tension forces based on the material and geometrical characteristics of the cable. The conversion considers the sag of the cable, assuming that the cable force is large enough for the static profile of the cable to be accurately described by a parabola. Such an assumption is almost always valid for cable-stayed bridges. Regardless of the module, App4SHM works in two modes. The training mode is used to collect observations under normal conditions, where the undamaged structure is operating under regular environmental and traffic conditions. The observations obtained in this way are used to train unsupervised machine learning algorithms to learn the normal behavior of the structure. The damage detection mode collects unlabeled observations which are tested against the normal behavior of the structure to detect abnormal behaviors that may be indicative of damage or excessive tensions. Despite the natural limitations of the accelerometers installed on smartphones, the frequency content of the accelerograms obtained with App4SHM is in accordance with that produced by professional accelerometers. However, frequencies larger than 25 Hz cannot be reliably recovered because of the limited sampling frequency (< 50 Hz) using smartphones. The paper illustrates the use of App4SHM for the measurement of cable tension forces in a large cable-stayed bridge undergoing retrofit, before and after the replacement of the cables.

A three-year project on Structural Health Monitoring of railway bridges: main results and lessons learnt

Bridges and viaducts worldwide are threatened by ageing, increasing loads and climate-related extreme events. The situation calls for immediate actions to prevent catastrophic failures and extend the life of our infrastructural heritage. In this regard, thanks to significant efforts from academics, Structural Health Monitoring is paving its way towards application in the real world. Unlike the simple monitoring activity, which is not new in the field, SHM is more identifiable as a paradigm, encompassing several steps and involving many stakeholders. Also, the focus is not on a project level but on the network as a whole. Such a new perspective offers the opportunity – and obliges at the same time – to look for synergies among different bridges, with the aim of designing standardized guidelines to foster SHM scalability. Embracing this vision, in 2020, RFI and the Mechanical Engineering Department of Politecnico di Milano gave birth to a collaboration oriented to testing the application of Structural Health Monitoring on the Italian railway infrastructure. The project had a twofold objective: on the one hand, the realisation of an automated software to extract and deliver information from the monitoring systems installed on three pilot bridges; on the other hand, the draft of guidelines that could report the lessons learnt and generalise as much as possible the design and exploitation of SHM on railway bridges. Given the interest in the feasibility of an extensive adoption of such practice, the pilot bridges were selected to be representative of the whole network. This paper wraps up the three-year project, intending to share the experience gained in the field and the take-home messages for future applications of SHM, with a focus on the key aspects for a broad spectrum adoption of this paradigm.

Evaluating Unmanned Aerial Vehicles for Vibrational Analysis: Exploiting Integrated Capabilities of Nano-Drones as Nodes in a WSN

Addressing the aging infrastructure challenge in both civil and industrial sectors has spurred the development of advanced sensor technologies and novel inspection methodologies, significantly contributing to the field of Structural Health Monitoring (SHM). While conventional sensors, such as structure-mounted accelerometers, have demonstrated high effectiveness, their deployment may be difficult or even impossible in high-risk environments like post-seismic areas, where the installation procedure hampers the operators' safety. Hence, this study aims to evaluate the characteristics of a novel sensing platform compatible with autonomous deployment for vibration inspection of infrastructures. In particular, we explore the use of Unmanned Aerial Vehicles (UAVs), with a specific focus on nano-drones, as a viable and promising solution due to their compact size and cost-effectiveness. In particular, Crazyflie 2.1 nano-drones are exploited as reference platforms, given their characteristics in terms of versatility and electrical parameters. The objective is to utilize the on-drone MEMS-based Inertial Measurement and Microcontroller Unit (IMU and MCU) to perform, at the same time, both flight control and navigation, and vibration inspection. This approach eliminates the need for additional hardware, going beyond other successful drone applications in SHM, such as visual inspections, sensors deployment, and distance measurements. The proposed solution is validated through a comparison with commercial sensors for SHM. Multiple drones can be combined to deploy wireless sensor networks in hard-to-reach locations, ready for vibration monitoring with minimal software adjustments, offering a novel approach to SHM. The results indicate that the Crazyflie drone is capable of detecting even small variations in the dynamic response of a beam, with a maximum deviation of 1% compared to commercially available counterparts designed for SHM purposes.

Towards reliability assessment of ultra-wideband microwave leakage monitoring in detecting stringer debondings in composite airframes

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. Among the multitude of possible microwave waveguides, recent works explored the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach. To proof the reliability of this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory to proof the methodology. Then, a large-scale fuselage segment was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. In addition, extending the same approach on a number of test cases, the probability of detection was carried out showing the minimum defect size which can be identify reliably. This allows designing a reliable structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.

Cumulative second harmonic Lamb wave generation and propagation in composite laminates: an insight into the influence of material anisotropy and damping

Significant strides have been taken in the structural design of fibrous composites in recent decades. Early detection of material degradation and damage in such materials is crucial for various industrial applications, yet poses challenges. Second harmonic Lamb waves have demonstrated their superior sensitivity to microstructural defects, making them an effective tool for structural health monitoring (SHM). However, understanding of the second harmonic Lamb wave generation and propagation in composite laminates remains inadequate due to the complexities introduced by material anisotropy and damping, which impede the practicality of SHM applications. This study investigates the cumulative second harmonic Lamb waves in composite laminates at low frequencies, considering both material anisotropy and damping. Theoretical analyses were carried out to explore how anisotropy and damping affect the generation and propagation of second harmonic Lamb waves. A nonlinear material constitutive model was proposed. It was used to conduct numerical simulations that verified the properties of second harmonic Lamb wave generation and propagation at different wave propagation angles, with a focus on the cumulative effect. Experiments were carried out to further validate the properties of the second harmonic Lamb waves in a carbon fiber reinforced panel. The findings indicate that the Lamb waves propagating at 0° showcase exceptional cumulative effect, rendering them suitable for SHM applications. Additionally, material damping induces a cumulative second harmonic Lamb wave with an initial increase followed by a decrease, creating a predictable “sweet” zone of larger amplitude that is easy to measure for SHM applications. The elucidation of the generation and propagation characteristics of the second harmonic Lamb waves provides guidance for structural damage detection and monitoring applications.

Impact of cryogenic environment on piezoelectric transducers used in SHM applications

A high number of actors of the space industry, including ESA (European Space Agency) and CNES (French national space agency), are engaged in a program aiming at developing future European reusable space launchers. In this context, PYTHEAS Technology has developed an embedded Non-Destructive Testing (eNDT) solution to ensure that strategic structural elements of the launcher are free of damage before a new launch, performing Structural Health Monitoring (SHM). The sensors used for this application are subject to severe temperature variations, including cryogenic temperatures. Cryogenic conditions are present in other fields requiring SHM solutions, such as liquid hydrogen reservoirs, increasing the importance of this study. Results of experimental tests are presented, where piezoelectric transducers are glued with two types of glue on samples of aluminum plates and carbon composite plates. A reference measurement is carried out before plunging the plates in a climatic chamber, down to -196°C. Pulse-echo and admittance measurements are performed during the temperature decrease and after the temperature rise. The admittance measurements show no deterioration of the samples after being immersed in liquid nitrogen: the transducers, wires, glue, and samples themselves have resisted to the temperature cycle. The pulse-echo signals’ time and frequency analysis show a linear attenuation of the signals relative to temperature. A difference in signal intensity and wave behavior has also been observed relative to the sample’s material: composite or aluminum. No difference regarding the type of glue is witnessed. These results pave the way for damage detection in harsh environments, since the system’s performance is maintained through a whole temperature cycle in cryogenic conditions.

Particle Filter for temperature compensation of FBG strain sensors for event segmentation

Structural Health Monitoring (SHM) plays a crucial role in assessing the integrity and performance of structures. Fibre-Bragg-Grating (FBG) technology has emerged as a valuable tool for strain measurements in SHM applications. However, FBG strain sensors are susceptible to temperature variations that can confound accurate strain measurements, potentially leading to errors in fatigue damage calculations and event detection. This research addresses the challenge of temperature compensation in FBG-based SHM setups. Instead of the traditional approach of placing temperature sensors near every strain sensor, which can escalate costs and complexity with a growing number of sensors, we propose a filter-based alternative. The proposed approach leverages the slower strain changes induced by temperature effects compared to those caused by external loads, e.g. a passing train. The key innovation lies in a filtering method that isolates temperature-induced strain variations as filtered data while treating load-induced strain as process noise. We explore the application of state-based filters such as the Kalman Filter (KF) and Particle Filter (PF). While the KF is limited to normally distributed noise, which does not represent load-induced strain effectively, the PF can handle various, adjustable noise distributions, that can be tailored to specific requirements. The research showcases the PF's ability for temperature compensation on multiple structures, such as a pedestrian bridge equipped with only 8 temperature sensors for 82 strain sensors loaded either in tension or compression. This novel approach enables more reliable strain measurements of events for detection and analysis in SHM systems.

A multivocal literature review of digital twins, architectures, and elements in civil engineering

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges

Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.

A Framework for Condition Survey and Structural Health Monitoring of Masonry Arch Bridges

Masonry arch bridges are essential elements of the road and railway networks of Europe, as they pertain to cultural heritage and play an important role in transportation. The integrity and safety of these centuries-old structures must be ensured. Therefore, there is great interest in understanding the behavior of masonry arch bridges and monitoring their health. Efficient methods for assessing the condition of structures, including inspections and monitoring, constitute a fundamental element of structural health monitoring (SHM) applications. Given the multitude of surveying techniques available, it becomes crucial to establish cost-effective sensor placement strategies capable of detecting structural failures. These strategies can involve identifying particularly sensitive areas, optimizing the number of sensors used, as well as refining the number and duration of monitoring setups, among other considerations. As part of the PONT3 project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in ageing bridges, this research focusses on developing interoperability-driven inspection and monitoring strategies to provide data that will allow anticipating the evolution of possible failure scenarios. In this regard, an extensive summary of the key characteristics of inspection and monitoring technologies, and typical system configurations in existing literature for masonry arch bridges will be provided. Then, the measurable physical parameters of interest for a specific failure propagation scenario will be identified using numerical simulations and applicable monitoring techniques for each of the physical parameters will be determined. Monitoring requirements in terms of measurement locations, accuracy, range, and temporal resolution will also be identified. The recommendations for interoperability-driven data collection to exchange and employ acquired information for the purpose of structural diagnosis will conclude the framework. The study will finally discuss a future outlook on the development of integrated SHM-oriented digital twins.

Structural Health Monitoring on Road Bridge: Application Cases of Optical Strand Sensor in JAPAN

In Japan, many of the major civil infrastructures such as road bridges, railways and tunnels was built during our high-growth period from the mid-1950s to the early 1970s. The number of these structures that has been more than 50 years old after construction, has been rapidly increasing in recent years. In this context, expectations for Structural Health Monitoring (SHM) have been rising to optimize its management and maintenance to extend its lifespan and ensure the safety of users. Although a variety of IoT sensors are developing and their precision is improving day by day, SHM still has crucial challenges, e.g., if it can really identify critical damages to structures, determine quantitative assessment of the serviceability limit, evaluate the safety of a structure quickly after major events such as earthquakes, or so on. In order to achieve our original goals, it is essential to accumulate practical applications of SHM by dedicating a specific sensor solution to a specific issue. The authors have introduced a unique and competitive Optical Strand Technology developed by OSMOS Groupe in France and have been providing the service of SHM mainly on civil infrastructures for more than 20 years in Japan. This paper presents some application cases of SHM to road bridges by means of Optical Strand Sensor which provide for different types of purposes and future prospects.

Elastodynamic shape derivative: focus on the sampling of a circular distribution

The detection and quantification of defects plays a crucial role in several industries. Assessing the severity enables to schedule maintenance appropriately, optimizing costs and reducing risks. Guided elastic waves are particularly suitable for Structural Health Monitoring applications on thin structures thanks to their long range and high sensitivity. Guided wave tomography, based on an acoustic propagation hypothesis, has been studied over the last decade to quantitatively image defects in waveguides. The acoustic approximation of this family of methods allows fast computation and good estimation of defects for simple geometries such as plates or pipes. However, this hypothesis induces that a single guided mode is considered, without mode conversion, which limits its use to defects larger than two wavelengths in structures close to ideal waveguides. Hence the need of new imaging algorithms for smaller defects in less ideal structures such as pipes with welded supports. We present here an adaptation of the shape derivative method to ultrasounds in order to reconstruct surface defect on structures with complex geometries. The physical model used is the full elastodynamic problem. It allows taking into account all physical phenomena occuring during wave propagation. Iterative methods such as shape derivative are well known to give precise results but at a high computational cost as the method needs to solve the full elastodynamic problem at each iteration, and a sensitivity to local minima, which may prevent convergence toward the true defect. To suppress these two drawbacks, a spectral finite element method is used to reduce the computation and memory costs. The result of acoustic guided wave tomography is also used as a first guess to reduce the number of iterations and the sensitivity to local minima. After explaining the principle of the shape derivative, validations on simulated and experimental data will be presented in this talk.

A piezoelectric vibration sensor with excellent performance based on Bi12SiO20 crystal for high-temperature applications

High-temperature piezoelectric vibration sensors play a crucial role in the accurate monitoring of the dynamic mechanical conditions in aerospace, automotive, and energy generation systems. However, the use of conventional piezoelectric materials in high-temperature environments is restricted owing to their limited Curie temperatures. In this study, we grew a piezoelectric crystal Bi12SiO20 (BSO) with the crystal cut optimized for high longitudinal piezoelectric coefficient and low piezoelectric crosstalk behaviors. Subsequently, a compression-type piezoelectric vibration sensor utilizing the BSO bulk crystal was developed and fabricated for structural health monitoring under high temperatures. The impact of pre-tightening torques on the sensor performance was investigated. Moreover, the sensor performance was analyzed under temperatures up to 650 °C. The BSO-based sensor exhibited an average sensitivity of ∼3.89 pC/g between 25 and 650 °C under 160 Hz frequency, with a variation of 5.5%. Additionally, the BSO-based sensor demonstrated ultra-stable sensitivity at 600 °C, highlighting its strong sensing capabilities and reliability under high temperatures. Thus, the BSO-based vibration sensor is a promising option for structural health monitoring applications under high temperatures.

Prediction of directivity characteristics of piezoelectric macro-fiber composite interdigital transducers: a semi-analytical approach

The employment of guided waves for structural health monitoring applications has attracted substantial attention in recent years. Piezoelectric transducers are frequently employed in these systems for elastic wave generation and acquisition. Their dynamic properties, i.e., direction-dependent frequency characteristics, are an important feature for designing a reliable monitoring strategy. Although analysis of the transducer directivity pattern can be performed using finite element models, these usually tend to be computationally very expensive, especially, for parametric and optimization studies. In this paper, a semi-analytical methodology is proposed that can predict the far-field responses of complex transducers of arbitrary shape and structure mounted on elastic plates. The approach reported here couples analytical far-field predictions with a local FE model evaluating the traction field generated by the transducer. Thus, the FE model captures the near-field responses, while the analytical part predicts the far-field responses. The implementation of this semi-analytical method requires development of analytical and numerical frameworks. The former includes computation of dispersion curves of the inspected plate, the stress and displacement profiles of wave modes and the excitability curves, while the latter includes estimation of the traction field between the transducer and the plate using the FE model. Owing to the popularity of piezoelectric macro-fiber composite (MFC) interdigital transducers, in this paper, we apply the proposed method to simulate the directivity patterns of the MFC IDTs of different configurations. To validate the reliability of the proposed method the simulated patterns are compared with experimental results obtained using Laser Doppler Vibrometer (LDV).