Transducer Network Research Articles

Printed PZT Transducers Network for the Structural Health Monitoring of Foreign Object Damage Composite Panel

The work presented here focuses on the structural health monitoring (SHM) of a foreign object damage (FOD) composite panel equipped with an innovative printed piezoelectric transducer network. The 3D woven composite FOD panel measures approximately 800 mm x 320 mm, is curved with a cross-sectional thickness varying from approximately 2 mm to 12 mm, and a stainless-steel leading edge is bonded at one of its sides. The core idea explored here is to rely on an innovative screen-printing technology to print a full piezoelectric transducer allowing to successfully achieve SHM on such a complex composite structure. This work is being carried out within the European project MORPHO – H2020. After printing a 25 elements PZT network, a four points bending fatigue experimental campaign using the PZT network along with other sensor technologies (embedded optical fibres with FBG sensors and acoustic emission sensors) is carried out. This unique experimental campaign allows to generate data and will help to develop diagnostic and prognostic methodologies for remaining life estimation and SHM of the FOD panel. It is demonstrated here through impedance measurements that the printing process associated with the printed PZT transducers is highly repeatable thus validating its use at a larger industrial scale. Furthermore, the printed piezoelectric transducers electromechanical behaviour is characterized and they are shown to be able to detect foreign object impact and to size and localize resulting damage using Lamb waves signals collected at different locations of the network. This innovative printing technology for PZT transducers network is thus extremely promising. It is furthermore highly advantageous to use the printed transducers for SHM instead of regular ceramic ones as this technology is non-intrusive, add negligible weight, can be printed during the manufacturing process, and arrays of transducers ensure easy availability of another transducer in case of failure of one.

A Composite Fuselage under Mechanical Load: a case study for Guided Wave-based SHM

The introduction of composite materials in aeronautics has brought numerous advantages, along with unique damage and failure modes. The structure health is currently ensured by a damage-tolerant design and non-destructive inspections. Among other techniques, Guided Wave-based Structural Health Monitoring (GW-SHM) has gained interest as a cost and time effective alternative to traditional non-destructive techniques. One of the main challenges for GW-SHM is the influence of environmental and operational conditions on the damage identification capability. Aircraft structures undergo a broad range of mechanical load conditions, affecting the GW-SHM system. The study of load influence is meaningful in view of applications where the SHM system is expected to work under varying load conditions. Such applications may be in-flight monitoring, but also on-ground only usage and structural test monitoring. A full-scale CFRP door surrounding structure was instrumented with a GW-SHM system and tested under mechanical load. A hydraulic test rig was used to apply three representative load cases in quasi-static conditions on the structure. A network of robust piezocomposite transducers to monitor the structure has been designed and manufactured. The network is organized in arrays, which include the transducers, cabling and a connecting base plate for optimized sensor installation. A multiplexing module is directly connected to the base plate enabling a fast and reliable sensor connection, a drastic cable weight reduction, and a modular design. The combined influence of load and temperature on the propagation of Guided Waves has been assessed. Finally, a data-driven approach to damage identification has allowed the detection and localization of barely visible impact damage introduced during the test campaign.

Diagnostic imaging of debonding in FRP-strengthened reinforced concrete structures using combinational harmonics generated by Rayleigh waves

A debonding location imaging technique (DLIT) utilising mixed-frequency Rayleigh waves is reported in this paper to predict the location of debonding in fibre-reinforced polymer (FRP) composite strengthened concrete structures. In the proposed methodology, a piezoceramic transducer network is employed to sequentially scan the FRP-concrete interface. One of the transducers serves as an actuator while the remainder serve as sensors. The incident waves which are sent by the actuator consist of two different fundamental frequencies. After the incident waves interact with debonding, additional signals are generated that coincide with the sum of the fundamental frequencies (i.e., sum frequency). The received wave component at the sum frequency is in accordance with theoretical predictions. In order to apply the DLIT, a time-frequency analysis is then applied to process the data collected by each actuator-sensor transducer pair in order to determine the signal energy density amplitudes (i.e., signal envelopes) at the fundamental frequencies and the sum frequency. Based on cross-correlation analysis between the signal envelopes of the fundamental frequencies and the sum frequency, an image can be constructed that reveals the centroid of the debonding region. Following the presentation of the proposed DLIT, numerical and experimental studies are then reported that compare the predicted centroid of debonding with the actual centroid of debonding as incorporated into the numerical model and the experimental specimens, respectively. The results demonstrate that the proposed DLIT based on the Rayleigh wave features at sum frequency can satisfactorily predict the location of debonding.

Nondestructive global corrosion measurement using guided wavefield data

Metallic structures often face degradation, and corrosion ranks among the most prevalent forms of deterioration. Accurate quantification of corrosion is crucial, especially for structures exposed to harsh environmental conditions, such as marine vessels and offshore installations. Because the traditional measurement methods based on scanning by ultrasonic gauge are time-consuming and provide only rough information on the thickness variability, there is a need to develop new robust and accurate diagnostics methods. This paper aims to investigate the corrosion monitoring of metal plates using guided wave propagation. Guided waves propagate within the entire volume of the specimen. If it is undamaged and isotropic, the velocity in all directions is the same, and the spreading wavefront takes a circular shape. The primary assumption presented in this paper is that the potential damage caused by corrosion leads to disturbance of this symmetry. The study shows Bilateral and Rotational Corrosion Symmetry Degree (BCSD and RCSD) functions, demonstrating a consistent decrease in symmetry values with increasing degree of degradation (DoD) caused by the corrosion process. The main aim of the study was to test the possibility of corrosion monitoring by using variable number of sensors comprising the transducer network. Also, the influence of the distance between the sensors affecting the size of the monitored area on the corrosion monitoring procedure was investigated. The paper contains the results of numerical and experimental campaigns conducted for corroded plates characterized by variable DoD and monitored using nine different transducers configurations. It is the first step in developing a novel measurement procedure specially designed for the ship and offshore industry. Therefore, because of the initial stage of the study, the last part of the paper discusses the limitations and drawbacks of the presented wave symmetry-based approach.

Damage assessment of laminated composites using unsupervised autonomous features

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

Nutrient-dependent signaling pathways that control autophagy in yeast.

Macroautophagy/autophagy is a highly conserved catabolic process vital for cellular stress responses and maintaining equilibrium within the cell. Malfunctioning autophagy has been implicated in the pathogenesis of various diseases, including certain neurodegenerative disorders, diabetes, metabolic diseases, and cancer. Cells face diverse metabolic challenges, such as limitations in nitrogen, carbon, and minerals such as phosphate and iron, necessitating the integration of complex metabolic information. Cells utilize a signal transduction network of sensors, transducers, and effectors to coordinate the execution of the autophagic response, concomitant with the severity of the nutrient-starvation condition. This review presents the current mechanistic understanding of how cells regulate the initiation of autophagy through various nutrient-dependent signaling pathways. Emphasizing findings from studies in yeast, we explore the emerging principles that underlie the nutrient-dependent regulation of autophagy, significantly shaping stress-induced autophagy responses under various metabolic stress conditions.

Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals

This paper presents a new structural health monitoring strategy based on a deep learning architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing-like debonds in lightweight stiffened composite panels (SCPs). Towards this, nonlinear finite element simulations of ultrasonic guided wave (GW) response of SCPs and laboratory-based experiments have been undertaken on multiple composite panels with and without baseplate-stiffener debonds using fixed a network of piezoelectric transducers (actuators/sensors).GW signals in the time domain are collected from the network of sensors onboard the SCPs and these signals in the frequency domain represent nonlinear signatures as the existence of higher harmonics. These higher harmonic signals are separated from the GWs (raw)and converted to images of time–frequency scalograms using continuous wavelet transforms. Adeep learning architecture is designed that uses the convolutional neural network to automatically extract the discrete image features for the characterization of SCP under healthy and variable breathing-debond conditions. The proposed deep learning-aided health monitoring strategy demonstrates a promising autonomous inspection potential with high accuracy for such complex structures subjected to multi-level breathing-debond regions.

Damage detection in composite laminates using nonlinear guided wave mixing

This paper presents a delamination damage detection technique based on the nonlinear response of Lamb waves in composite laminates. In the approach, a network of transducers is employed to sequentially scan the structure by actuating and receiving dual guided waves. Using combined frequency waves originated due to contact nonlinearity when the incident dual frequency wave interacts with the damage, a damage localization imaging algorithm is proposed to locate the damage and determine the damage location. The proposed damage localization imaging algorithm is based on analysing the cross-correlation of the signal envelope at combined frequency wave with the excitation pulse envelope at incident waves for each transducer pair, the damage localization image is constructed. A numerical study using an experimentally validated finite element model is used to verify the proposed damage detection technique. The results of the numerical study show that, despite the small amplitude of the nonlinear components, the proposed method can effectively predict the damage location, and it does not require baseline measurements, which makes it potential to be a baseline-free damage detection technique.

Fatigue-Crack Detection in a Multi-Riveted Strap-Joint Aluminium Aircraft Panel Using Amplitude Characteristics of Diffuse Lamb Wave Field

Structural health monitoring of riveted aircraft panels is a real challenge for maintenance engineers. Here, a diffused Lamb wave field is used for fatigue-crack detection in a multi-riveted strap-joint aircraft panel. The panel is instrumented with a network of low-profile surface-bonded piezoceramic transducers. Various amplitude characteristics of Lamb waves are used to extract information on fatigue damage. A statistical outlier analysis based on these characteristics is also performed to detect damage. The experimental work is supported by simplified modelling of wave scattering from crack tips to explain complex response features. The Local Interaction Simulation Approach (LISA) is used for this modelling task. The results demonstrate the potential and limitations of the method for reliable fatigue-crack detection in complex aircraft components.

Cracking and Fiber Debonding Identification of Concrete Deep Beams Reinforced with C-FRP Ropes against Shear Using a Real-Time Monitoring System.

Traditional methods for estimating structural deterioration are generally costly and inefficient. Recent studies have demonstrated that implementing a network of piezoelectric transducers mounted to critical regions of concrete structural members substantially increases the efficacy of the structural health monitoring (SHM) method. This study uses a recently developed electro-mechanical-admittance (EMA)-based SHM system for real-time damage diagnosis of carbon FRP (C-FRP) ropes installed as shear composite reinforcement in RC deep beams. The applied SHM technique uses the frequency response measurements of a network of piezoelectric lead zirconate titanate (PZT) patches. The proposed strengthening methods using C-FRP ropes as ETS and NSM shear reinforcement and the applied anchorage techniques significantly enhanced the strength and the overall performance of the examined beams. The retrofitted beams exhibited increased shear capacity and improved post-peak response with substantial ductility compared with the brittle failure of the non-strengthened specimens. The health condition and the potential debonding failure of the applied composite fiber material were also examined and quantified using the proposed SHM technique. Damage quantification of C-FRP ropes is achieved by comparing and assessing the values of several statistical damage indices. The experimental results demonstrated that the proposed monitoring system successfully diagnosed the region where the damage occurred by providing early warning of the forthcoming critical shear cracking of concrete and C-FRP rope debonding failures. Furthermore, the internal PZT transducers showed sound indications of the C-FRP rope's health condition, demonstrating a direct correlation with the mechanical performance of the fibers.

Continuous baseline update using recurrence quantification analysis for damage detection with guided ultrasonic waves

For the safe operation of vehicles and full utilization of lightweight materials, assurance of structural integrity is a prerequisite at all times. Structural health monitoring with permanently installed transducers offers a great advantage for primary load-bearing structures of all means of transportation and other safety-relevant components such as hydrogen tanks: allowing damage detection during operation. One means to detect internal defects is the method of guided ultrasonic waves (GUWs), which can be generated and recorded by piezoelectric transducers. GUWs propagate along the elongated dimension of a structure, and a transducer network can completely cover and monitor structures. Defects can alter the signal along affected paths and allow for their detection. However, a challenge and obstacle for the application of such a testing technique in the service of means of transportation is the large influence of temperatures. These influences are difficult to distinguish from the effect of defects. One approach to overcome this difficulty is the “continuous baseline update”. Recurrence quantification analysis is tested and compared to established features as a new approach to “continuous baseline update” in this paper. Publicly available GUW data (http://www.openguidedwaves.de/) recorded under varying temperature conditions have been used to show how the methods perform. They reliably separate temperature and damage effects, while the recurrence quantification analysis yields the best results.

Damage localization and characterization using one-dimensional convolutional neural network and a sparse network of transducers

Early damage identification and continuous system monitoring save dramatically maintenance costs and increase the lifespan of priceless structures. Convolutional neural networks (CNNs) have attracted the attention of the structural health monitoring (SHM) community in recent years due to their great potential for identifying underlying data patterns. However, employing two-dimensional convolutional layers in a CNN necessitates the use of strong computing resources. Therefore, based on the present state-of-the-art technical solutions, a two-dimensional CNN is not suitable for real-time SHM applications with stand-alone processing units. One-dimensional convolutional networks (1D-CNN) have recently been employed in Ultrasonic Guided Wave-based (UGW-based) damage detection to address the aforementioned disadvantage. In this paper, a methodology for damage assessment at three levels – detection, localization, and characterization – based on 1D-CNN is put forward. Furthermore, the sequence length of the time-domain signals is significantly shortened by the application of a novel approach for processing them. Additionally, it is shown to what extend this method can improve the distinguishability between datapoints obtained from various damage scenarios. Consequently, by reducing the dimensionality of the problem, the proposed approach significantly reduces the memory usage of the classification algorithm. Experimental measurements as well as Numerical simulations, in which various damage scenarios such as corrosion, circular hole and cracks have been considered, are carried out to evaluate the efficacy of the proposed algorithm. It is shown that the suggested approach has benefits in terms of true classification rate of instances (above 93 percent for detection, localization, and characterization), computing time, in-situ monitoring, and noise resilience.

An efficient Lamb wave-based virtual refined time-reversal method for damage localization in plates using broadband measurements

This study proposes a modified virtual time-reversal (VTR) algorithm for baseline signal-free damage detection in plate-like structures. The physical actuation and sensing of Lamb waves are performed using a broadband Gaussian excitation instead of the conventional narrowband modulated tone burst excitations. The forward response and the reconstructed signal due to the time-reversal process for a narrowband input signal are then constructed virtually using the broadband transfer function. The method eliminates the possibility of numerical errors encountered in the conventional VTR method based on narrowband excitations. It is also more efficient than the traditional VTR algorithm because it can probe at multiple excitation frequencies using a single measurement for each sensing path. This modified VTR algorithm is employed in the recently developed refined time-reversal method (RTRM), which uses an extended signal length of the reconstructed signal for computing damage index (DI) and probes the structure at the best reconstruction frequency. The new method is termed the virtual refined time-reversal method (VRTRM). The DIs based on the VRTRM are used in the reconstruction algorithm for probabilistic inspection of defects to achieve baseline signal-free localization of damages. The efficacy of the proposed VRTRM for damage localization is experimentally verified with the established technique RTRM. Experiments are performed in an aluminium plate equipped with a network of surface-bonded piezoelectric patch transducers to illustrate the conventional VTR’s pitfalls and the modified VTR’s accuracy for a single mass damage scenario. The results show that the proposed VRTRM is as accurate as the established technique RTRM in estimating the reconstructed signals and localizing a block mass damage. Finally, the VRTRM is shown to localize in a dual damage scenario with excellent accuracy. In contrast, the conventional main mode-based VTR method fails to localize the damages with or without single-mode tuning.

Identification and Characterization of SOG1 (Suppressor of Gamma Response 1) Homologues in Plants Using Data Mining Resources and Gene Expression Profiling.

SOG1 (Suppressor of the Gamma response 1) is the master-regulator of plant DNA damage response (DDR), a highly coordinated network of DNA damage sensors, transducers, mediators, and effectors, with highly coordinated activities. SOG1 transcription factor belongs to the NAC/NAM protein family, containing the well-conserved NAC domain and five serine-glutamine (SQ) motifs, preferential targets for phosphorylation by ATM and ATR. So far, the information gathered for the SOG1 function comes from studies on the model plant Arabidopsis thaliana. To expand the knowledge on plant-specific DDR, it is opportune to gather information on other SOG1 orthologues. The current study identified plants where multiple SOG1 homologues are present and evaluated their functions by leveraging the information contained in publicly available transcriptomics databases. This analysis revealed the presence of multiple SOG1 sequences in thirteen plant species, and four (Medicago truncatula, Glycine max, Kalankoe fedtschenkoi, Populus trichocarpa) were selected for gene expression data mining based on database availability. Additionally, M. truncatula seeds and seedlings exposed to treatments known to activate DDR pathways were used to evaluate the expression profiles of MtSOG1a and MtSOG1b. The experimental workflow confirmed the data retrieved from transcriptomics datasets, suggesting that the SOG1 homologues have redundant functions in different plant species.

Study of Multistage Damage Detection Method Based on Lamb Waves and Thermal Effect

Non-destructive testing has been implemented in many industries to ensure structural safety and reliability by detecting defects. In this study, Lamb waves are used for early damage detection and characterization. The aim of this research work is to develop a methodology for damage characterization (detection, localization, severity, and life estimation). Different problems associate with Lamb waves propagation as their dispersive behavior, and the effect of varying temperature on the amplitude and arrival time of the wave. Current study focuses on investigating the multistage damage detection method based on Lamb waves. It uses a network of transducers to improve damage detection and localization. It is aimed to validate the present technique by ellipse method to improve damage localization of the previously detected damages. To model the first stage of the technique, numerical simulations were performed on an 800x800x1.293 mm aluminum plate, using FE software ABAQUS explicit. Obtained signals from the simulation can detect s the presence of damage by comparing the baseline signal with damaged signals. The corresponding delays have been validated and compared with those of literature to improve the estimation of damage position.

Multi step structural health monitoring approaches in debonding assessment in a sandwich honeycomb composite structure using ultrasonic guided waves

This paper aims to investigate the use of ultrasonic guided wave (GW) propagation mechanism and the assessment of debonding in a sandwich composite structure (SCS) using a multi-step approach. Towards this, a series of GW propagation-based laboratory experiments and numerical simulations have been carried out on the SCS sample. The debonding regions of variable size and locations were assessed using a pre-defined network of piezoelectric lead zirconate transducers (PZT). Besides, several artificial masses were also placed in the SCS to validate the multi-step structural health monitoring (SHM) strategy. The SHM approach uses a proposed quick damage identification matrix maps and an improved elliptical wave processing (EWP) strategy of the registered GW signals to detect the locations of debonding and other damages in the SCS. The benefit of the proposed damage identification map is to locate the damaged area (sectors) quickly. This identification step is followed by applying the damage localization step using the improved EWP only on the previously identified damage sector region. The proposed EWP has shown the potential to effectively locate the hidden multiple debonding regions and damages in the SCS with a reduced number of calculations using a step-wise approach that uses only a selected number of grid points. The paper shows the effectiveness of the proposed approach based on data gathered from numerical simulations and experimental studies. Thus, using the above-mentioned SHM strategy debondings and damages present within and outside the sensor network are localized. The results were cross verified with nondestructive testing (NDT) methods such as infrared thermography and laser Doppler vibrometry.

Electromechanical impedance based debond localisation in a composite sandwich structure

An electromechanical impedance (EMI) based structural health monitoring (SHM) approach is proposed for the localisation of skin-core debonds in composite sandwich structure (CSS). Towards this, laboratory experiments and numerical simulations of EMI in a CSS with core to bottom face-sheet debond have been carried out using a network of piezoelectric transducers (PZTs). The frequency-domain analysis of the registered EMI signals shows that the presence of inter-facial debonds in the CSS significantly influences the conductance magnitudes of the registered EMI data. It was also noticed that the conductance magnitudes of the signals are dependent on the debond-to-PZT distances. In all the study cases, an agreement between the simulation and experimental results is observed. Eventually, a simulated SHM approach is proposed that uses a debond detection algorithm to calculate the changes in conductance magnitudes to effectively locate such debonds in CSS. The study is further extended for the detection of debonds at different locations in the CSS, including a debond located at the edge to assess the potential of the proposed SHM approach.

Methodology of neural network compression for multi-sensor transducernetwork models based on edge computing principles

This paper focuses on the development of a methodology to compress neural networks thatis based on the mechanism of prun-ingthe hidden layer neurons. The aforementioned neural networks are created in order to process the data generated by numerous sensors present in a transducer network that would be employed in a smart building. The proposed methodology implements a single approach for the compression of both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) that are used for the tasks of classification and regression. The main principle behind this method is based on the dropout mechanism, which is employed as a regulation mechanism for the neural networks. The idea behind the method proposed consists of selecting optimal exclusion probability of a hidden layer neuron, based on the redundancy of the said neuron. The novelty of this method is theusage of a custom compression network thatis based on an RNN, which allows us to determine the redundancy parameter not just in a sin-gle hidden layer, but across severallayers. The additional novelty aspect consists of an iterative optimization of the network-optimizer, to have continuous improvement of the redundancy parameter calculator of the input network. For the experimental evalu-ation of the proposed methodology, the task of image recognition with a low-resolution camera was chosen, the CIFAR10 dataset was used to emulate the scenario. The VGGNet Convolutional Neural Network, that contains convolutional and fully connected lay-ers, was used as the network under test for the purposes of this experiment. The following two methods were taken as the analogous state of the art, the MagBase method, which is based on the sparcification principle as well as the method which is based on rarefied representation by employing the approach of rarefied encoding SFAC. The results of the experiment demonstrated that the amount of parameters in the compressed model is only 2.56% of the original input model. This has allowed us to reduce the logical output time by 93.7% and energy consumption by 94.8%. The proposed method allows to effectively usingdeep neural networks in transducer networks that utilize the architecture of edge computing. This in turn allows the system to process the data in real time, reduce the energy consumption and logical output time as well as lower the memory and storage requirements of real-world applications.

Customizable Aptamer Transducer Network Designed for Feed-Forward Coupling.

Solution-based biosensors that utilize aptamers have been engineered in a variety of formats to detect a range of analytes for both medical and environmental applications. However, since aptamers have fixed base sequences, incorporation of aptamers into DNA strand displacement networks for feed-forward signal amplification and processing requires significant redesign of downstream DNA reaction networks. We designed a novel aptamer transduction network that releases customizable output domains, which can then be used to initiate downstream strand displacement reaction networks without any sequence redesign of the downstream reaction networks. In our aptamer transducer (AT), aptamer input domains are independent of output domains within the same DNA complex and are reacted with a fuel strand after aptamer–ligand binding. ATs were designed to react with two fluorescent dye-labeled reporter complexes to show the customizability of the output domains, as well as being used as feed-forward inputs to two previously studied catalytic reaction networks, which can be used as amplifiers. Through our study, we show both successful customizability and feed-forward capability of our ATs.

Detecting Stress Changes and Damage in Full-Size Concrete T-Beam and Slab with Ultrasonic Coda Waves

AbstractThis article describes the use of coda wave measurements in a network of transducers for detecting stress changes and damage in large concrete structures. The applicability of the presented...