bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Crack Tip Position in Adhesively Bonded Joints Under Mixed I+II Mode Loading
Authors:   Politecnico di Milano, Panerai Alessandra ,   Politecnico di Milano, Bernasconi Andrea ,   Politecnico di Milano, Carboni Michele
Published: June 2024

The use of adhesive joints has gained favour in recent years, due to their capacity for weight reduction, improved stress distribution, and enhanced fatigue resistance. However, the widespread use of adhesive joints in structural applications is hindered, because reliable and mature inspection and defect detection techniques are not yet available. Adhesive joints in structural applications are subject to fatigue loading, during which debonds can initiate and propagate, ultimately leading to potential failure. In the majority of cases, joints operate under mixed-mode loading conditions. In this work, steel Cracked Lap Shear specimens were tested to investigate the crack growth behaviour of adhesive joints under mixed I+II mode loading. To address the challenge of monitoring these joints, a range of experimental techniques was employed. These techniques included Visual testing, Digital Image Correlation, Optical Backscatter Reflectometry, and localization by Acoustic Emission. The different methods were evaluated and compared to determine their effectiveness in locating the crack tip within the joints during fatigue propagation.
Modelling the Relationship between Water Jetting Parameters and Material Failure in Heat Exchanger Tubes
Authors:   HZ University of Applied Sciences, Saad Sondos ,   Dow Benelux b.v., Borgt Hans ,   HZ University of Applied Sciences, Naughton Padraig
Published: June 2024

The Zeeland-Flemish region has a robust process industry where heat exchangers (HEX) are essential for managing heat transfer. However, Fouling in HEX occurs when deposits of contaminants form on surfaces affecting heat transfer. HEX maintenance/cleaning can harm the material of construction (MoC). This study explores how altering Water Jetting (WJ) parameters affects MoC failure mechanisms. This knowledge can optimize processes to prevent unwanted MoC failure while achieving desired fouling control.
In this study, a three-factorial experiment was designed to investigate how increased water pressure and flow rates impact the structural health of copper used in HEX tubes during WJ cleaning. Then, we compared these results with experiments using steel as the MoC. Statistical and mathematical analysis were employed to understand the effect of process parameters on damage mechanisms and identify the most influential parameter. For copper samples, we observed that higher water pressure consistently increased damage across all flow rates, following a linear trend of α_p =0.001(gm loss/gm sample. Kpsi). Flow rate's impact on damage exhibited logarithmic behavior, which can be split into two ranges. At low to medium flow levels, increased flow rates raised damage at a rate of α_f =0.007(gm loss/gm sample. gpm). However, at high flow rates, the increase in flow had no effect on the damage as it was attributed solely to pressure.
Comparing steel and copper at high pressure levels, it was found that steel experienced a lower linear increase in damage, β_f =0.0036(gm loss/gm sample. gpm), compared to α_f for copper.
These results offer insights for optimizing water jetting cleaning processes to minimize material damage during HEX maintenance, enhancing overall efficiency.
Usage and structural health monitoring systems in military tactical transport aircraft
Authors:   Airbus Defence & Space, Military Aircraft, Iñesta Daniel ,   Airbus Defence & Space, Military Aircraft, Caffyn Pablo ,   Airbus Defence & Space, Military Aircraft, Alonso Jaime Garcia
Published: June 2024

The variability of use in the military tactical transport aircraft is not always well captured in the intended use declared by the operators. On the other hand, the computers installed in the aircraft are already collecting huge amount of data that can be downloaded on ground with no additional burden. With the properly data analysis and management on ground, and by using the existing flight physics models of the aircraft or by training neuronal networks it is possible to use the data to obtain relevant information. Such as usage figures, detect potential overloads, obtain loads and stresses several times by second. Those loads and stress time histories can then use to re-calculate the fatigue and crack propagation is the same way as per certification in a cycle-by-cycle basis, then re-evaluate the maintenance to optimized it according to real use of the aircraft. Produce operational recommendations and perform prognosis analysis considering the past and intended use in the future. On the other hand, for local areas prone to accidental damage, direct sensor measurements can be used for damage monitoring.
Alternative coded excitation for multi-channel low-power ultrasonics
Authors:   Imperial College London, Challinor Connor ,   Imperial College London, Cegla Frederic
Published: June 2024

Coded excitation is a well-researched signal processing technique that employs phase modulations to improve signal quality in acquired A-scan ultrasonic timetraces. By utilising phase modulations based on sequences with favourable time-compression properties, total excitation energy can be increased. This directly improves Signal-to-Noise Ratio (SNR) without necessitating an increase in peak output power or reducing range resolution. Most prior research into this topic has focussed on understanding how typical ultrasonic inspection systems can be improved by incorporating coded excitations. This work instead highlights the benefits of an ultrasonic inspection system that is designed especially for coded excitations.

Traditional systems ensure a high SNR by using high-voltage excitations, isolation circuits and pre-amplifiers before received signal digitisation. Instead, in this work we propose the use of low-voltage transmissions amplitudes which are within the operating range of the receiver components such that receiver isolation is not required. This removes the limitation on applicable sequence lengths for coded excitation measurements. Therefore, ever increasing sequence lengths can be utilised to improve the measurement SNR without increasing signal dead-zone. Further, this opens the possibility of using different types of sequence which can achieve quasi-ideal compression from a single transmission event, and facilitates the use of quasi-orthogonality for suppressing crosstalk during simultaneous operation of multi-channel systems.

Through physical experiments, we show how this low-power coded approach can be utilised to match the performance (45dB SNR) of conventional high voltage systems (200V transmission amplitude with 15dB receiver amplification) whilst using only +/-2V transmission signals (40dB reduction in peak excitation power). The ability to remove inefficient high-voltage transmission hardware by adopting this technique can open the door for equally capable but much more cost effective and compact ultrasonic inspection hardware, making it ideal for applications such as permanently installed systems for Structural Health Monitoring.
Concrete crack pixel-level segmentation: a comparison of scene illumination angle of incidence
Authors:   University of Strathclyde, Dow Hamish ,   University of Strathclyde, Perry Marcus ,   University of Strathclyde, McAlorum Jack ,   University of Strathclyde, Pennada Sanjeetha
Published: June 2024

Previous research has demonstrated how angled and directional lighting can enhance the detection of concrete cracks in low-light environments and outperform diffused lighting alternatives. This paper investigates the effect of different angles of incidence of directional lighting for concrete crack pixel-level segmentation. Five directional lighting datasets of cracked concrete slabs were captured, each using an angle of incidence of 10, 20, 30, 40, and 50 degrees, respectively. A directional lighting crack segmentation algorithm was applied to each lighting angle dataset. Algorithm output comparisons with ground truths revealed that the directional lighting method performed best on the 50-degree lighting dataset, obtaining a recall, precision, and F1 score of 68%, 81%, and 74%, respectively. However, qualitative analysis of the segmentation outputs on a sub-image scale revealed that towards the edges of the images, the segmentation performance of 30-degree lighting was significantly better, with results closely matching those of the ground truth. This research highlights that the lighting angle of incidence can increase the performance of directional lighting concrete crack segmentation depending on defect position. The results from this work have the potential to improve low-light environment concrete crack detection and monitoring.
Reconstruction of direct waves between passive flexible sensors using diffuse ultrasonic waves
Authors:   Xiamen University, LAI Wenxin ,   Xiamen University, Xu Shuang ,   Xiamen University, LUO Zechen ,   Xiamen University, CHEN Paixin ,   Xiamen University, YAN Honglin ,   Xiamen University, Kai WANG Kai
Published: June 2024

The sensing system that enables generation and sensing of guided ultrasonic waves (GUWs) serves as a pivotal cornerstone for the structural health monitoring based on GUWs. Traditional sensing systems have been constrained by inherent limitations including substantial mass addition and limited flexibility, and this leads to an increasing research efforts for the emerging nano-composite sensors. Despite the superior properties of lightweight and flexibility, the nano-composite sensors is incapable of active wave excitation, and thus waves propagating directly between sensors cannot be obtained. To tackle this deficiency, an approach grounded in representation theorem to reconstruct signals using diffuse ultrasonic waves is proposed. In this approach, the waves propagating between two passive sensors can be approximated through the cross-correlation of wave signals in a diffused status. On this basis, the passive sensors can be virtually converted to active actuators, enabling the acquisition of waves virtually excited and captured by a pair of passive sensors. In experimental validations, the wave signals between passive sensors are retrieved and the damage close to the sensing path is detected. The proposed method enhances signal acquisition capability of sensing networks based on flexible passive sensors and opens avenues for the monitoring of previously inaccessible blind areas within complex structures.
Analytical insight into local defect resonance using a two-step method
Authors:   Xiamen University, LAI Wenxin ,   Xiamen University, Xu Shuang ,   Xiamen University, LUO Zechen ,   Xiamen University, CHEN Paixin ,   Xiamen University, WANG Kai
Published: June 2024

Local defect resonance (LDR) has gained increasing attention in the field of non-destructive testing due to its capacity to selectively amplify vibrations within the defect regions and to enable defect-selective and high-resolution imaging. How-ever, the conventional theory based on vibration method adopted for analyzing the fundamental frequencies of LDR deviates from experimental results when dealing with relatively thick defects. To tackle this deficiency, this study presents a two-step analytical methodology designed to precisely evaluate the local reso-nance effects of thick planar defects. In the first step, the normal-mode expansion method is employed to calculate the phase shift of the reflected elastic wave. In the second step, by using the standing wave's wavelength obtained in the first step, the Rayleigh method is taken into account to calculate LDR frequencies for various defect shapes. The refined theory is subsequently validated through finite element analysis and experimental measurement. The results clearly demonstrate that the LDR frequencies calculated using the refined theory exhibit a better agreement with experimental and simulated data.
Insight into local defect resonance and its in-situ measurement based on flexible nano-composite sensors
Authors:   Xiamen University, Xu Shuang ,   Xiamen University, WANG Kai ,   Xiamen University, LAI Wenxin ,   Xiamen University, LUO Zechen ,   Xiamen University, CHEN Paixin ,   Xiamen University, YAN Honglin ,   Huaqiao University, Guan Ruiqi
Published: June 2024

The local defect resonance has been proven to be effective for damage detection, and the methods based on LDR for damage evaluation have demonstrated appealing capabilities of selective assessment and applicability to complex structures. To explain the generation of LDR, models based on vibration theories have been developed in which the defect boundaries are assumed to be fixed. Nevertheless, existing models can only give an accurate prediction of frequency for fundamental LDR mode owing to the deviation of their assumptions from the reality. In addition, the measurement of LDR is currently limited to noncontact technologies, hampering its application to in-situ and online monitoring of inspected structures. To overcome these deficiencies, an analytical model is proposed in this investigation to gain an insight into the generation of LDR from the perspective of wave reflection at defect boundaries and wave superposition. In this model, the interaction of guided waves with defect is scrutinized using a normal mode expansion method to obtain the phase shift of the reflected waves at the defect boundaries. On this basis, the formation of standing waves is analyzed to interpret the occurrence of the resonance of guided waves, whereby the relation of the LDR frequencies and the defect parameters can be obtained. On top of this analytical investigation, the sensing of the LDR using a novel nano-composite sensor is attempted. Flexible and lightweight, these sensors can form a dense sensing network, with which the LDR response in the inspected region can be perceived. Using the LDR-based method and nano-composite sensors, this approach can give a damage characterization in an in-situ and online manner for complex structures. Experimental validations of the proposed approach is performed in which delaminations in composite structures are identified and assessed using the LDR response perceived with the nano-composite sensors.
Rejection of parametric model errors in a recursive Kalman filter for virtual sensing applications
Authors:   Aarhus University, Greś Szymon ,   ETH Zürich, Tatsis Konstantinos E. ,   Université Gustave Eiffel, Döhler Michael ,   ETH Zürich, Dertimanis Vasilis K. ,   ETH Zürich, Chatzi Eleni
Published: June 2024

The performance of the Kalman filter is often hindered by the discrepancies between a model used to realize the filter and the true model of the data-generating system. While some methods to account for those errors exists, the majority is restricted to Luenberger's observers. The objective of this work is to develop a Kalman filter where the effect of parametric model errors is rejected from the state and observation equations before the development of the state estimate, and without the explicit knowledge of the error terms. For this purpose, errors in a physical parameter of a parametrized mechanical system are modelled as additive terms in the state and observation equations by means of first-order perturbation analysis. The rejection step is achieved with an injection of a shaped output nullifying the effect of the added terms. A state observer is then derived under the assumption that the injected shaped outputs and the system states are uncorrelated. The performance of the proposed approach is illustrated on an experimental data of a plate system.
A dimensionless feature for continuous bridge health monitoring with tiltmeter measurements
Authors:   University of Parma, Battista Gianmarco ,   University of Parma, Pavoni Stefano ,   University of Parma, Montepara Antonio ,   University of Parma, Vanali Marcello
Published: June 2024

Structural Health Monitoring (SHM) is an essential tool for bridges especially if they have a long period of activity. The bridge considered in this work allows the crossing of the river Po between Lombardia and Emilia-Romagna in Italy. It is about 1.2 km long and has 35 main spans: 25 simply supported beams and 10 cantilevers. The bridge, in use since 1958, underwent a closure period and extraordinary maintenance in 2019 due to severe structural problems. After reopening, the bridge is continuously monitored with a system of 88 bi-axial tiltmeters installed in May 2022. The sensors measure static inclinations (< 1Hz) providing information on the deflection and torsion of the bridge spans. The monitoring system returns a set of summary statistics for each inclination signal gathered in the last hour. Statistics like the mean are used to examine anomalous span drifts in the long term. Instead, the Interpercentile Range is used to evaluate span movements in the short term. In fact, the latter proved to be a good indicator of the hourly traffic load on the bridge, assuming a constant structure stiffness. Therefore, an SHM strategy is to monitor span movements, on a daily basis, using traffic as an active load. This makes it possible to identify any increases in bridge oscillations in relation to a reference condition, calculated over a given monitoring period. For this purpose, a dimensionless metric is proposed as monitoring feature. First, a theoretical analysis of metrics is presented. Then, results obtained from experimental bridge monitoring data are shown.
Application of Structural Health Monitoring to the historical Monument to Four Days of Naples
Authors:   University of Naples Federico II, Spizzuoco Mariacristina ,   University of Naples Federico II, Katouli Habibollah ,   University of Naples Federico II, De stefano Tommaso ,   Ing. Filippo Cavuoto srl, Cavuoto Filippo ,   BOVIAR s.r.l., Bovio Giuseppe ,   University of Naples Federico II, Serino Giorgio
Published: June 2024

Heritage structures have a significant role in the nation’s history: the preservation, protection and if needed restoration of the heritage structures is a concern of the governing authorities.
To understand the change in geometry, material, boundary conditions and loading, compared to its original state, is known as Structural Health Monitoring (SHM) if the damage assessment is continued to observe day by day variations. The main goal of SHM is to estimate the dynamic response of the monitored buildings in urban areas to daily stresses produced by environmental and anthropogenic factors (variations in ambient temperature and humidity, wind velocity, vibrations produced by vehicular or railway traffic, etc.). Continuous monitoring also makes it possible to assess the impact of extraordinary events such as extreme weather events, earthquakes, excavations, cultural events involving many people nearby the monitored buildings.
The above considerations become highly significant when referring to historical heritage.
An application of SHM is the “Monument to Four Days of Naples” (representing some scenes of the insurrection occurred in 1943 to release Naples), built by Marino Mazzacurati in 1964 and consisting in a wide basement and four stelas made of ignimbrite. In order to provide stability against seismic excitation, a base isolation system has been properly designed and mounted to prevent the stelas’ overturning. Due to the historical and artistical importance of the monument, a Structural Health Monitoring System has been activated to check the dynamic response of the structure under environmental vibrations produced by the vehicular traffic and the nearby Metro Line 6, and under a seismic event. The proposed paper is within the framework of the emerging research field designated as urban seismology, that has both seismological objectives, as obtaining better microzonation maps in highly populated areas, and engineering objectives, as the monitoring of traffic or the surveying of historical buildings.
Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing
Authors:   BAM Federal Institute for Materials Research and Testing, Karapanagiotis Christos ,   BAM Federal Institute for Materials Research and Testing, Schukar Marcus ,   BAM Federal Institute for Materials Research and Testing, Breithaupt Mathias ,   BAM Federal Institute for Materials Research and Testing, Duffner Eric ,   BAM Federal Institute for Materials Research and Testing, Ulbricht Alexander ,   BAM Federal Institute for Materials Research and Testing, Prager Jens ,   BAM Federal Institute for Materials Research and Testing, Krebber Katerina
Published: June 2024

We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future.
Monitoring of ground and building vibrations caused by train wheel defects
Authors:   Witteveen+Bos, De Bruijn Janno ,   Delft University of Technology (TU Delft), Besseling Floris
Published: June 2024

This paper describes a study performed on train wheel defects and their effect on rail vibrations. The paper describes a test setup allowing the researchers to capture ground vibrations at the desired level of detail to prove to what extend wheel quality influences ground vibration levels and associated

nuisance to people. The analysis of the measurement signals shows that the individual axles of a passing train can be detected excellently with the signal from the axle detection and the vibration sensors. The signals from various sensors in the measurement setup are synchronized with high time accuracy, making it possible to overlay the time interval of an axis passage over the signal from the vibration measurements. This makes it possible to track vibrations at bogie and axle level. The distance of measurement points from the track is ultimately the determining factor for the extent to which vibrations are dominated by an axle, a bogie, a wagon or the entire train. In addition, direct wheel roundness measurements have been performed for trains that appear to have defect wheels based on ground vibration data. With the track based measurements, the ground vibration measurements and the direct wheel measurements an unique data set has been generated, allowing for an evaluation of the importance of defect train wheel from the perspective of vibrations nuisance to the environment. Relations of measured vibrations to other parameters, both rolling stock related and environmental parameters were investigated based on the generated set of data. The analysis on the data reveals interesting relations between train wheel parameters and ground vibrations, which are discussed in this paper.
Dispersion aware matching pursuit decomposition of Lamb wave signals for structural health monitoring of thin plates structures
Authors:   ParisTech, Rodriguez Sebastian ,   ParisTech, Paunikar Shweta ,   ParisTech, Margerit Pierre ,   ParisTech, Monteiro Eric ,   ParisTech, Chinesta Francisco ,   ParisTech, Mechbal Nazih ,   ParisTech, Rébillat Marc
Published: June 2024

Structural Health Monitoring (SHM) techniques are key to monitor in real time the health state of engineering structures, where damage type, location and severity are to be estimated by applying signal processing and deep learning techniques to temporal signals measured by sensors coming from the structure under study. Among the most widely used signal processing techniques in the SHM community is the Matching Pursuit Method (MPM), which allows to extract key features from a measured signal. MPM simply consists in approximating temporal data as a sum of different terms composed of a delayed and scaled signal called atom. This signal decomposition approach is however limited to non-dispersive signals which is not the case when dealing with Lamb waves signals widely used in the SHM of thin structures. In this case, in addition of delays and attenuations, wave packed also endure dispersion caused by the fact that all the frequencies do not propagate at the same speed within the structures under study. In this context, an improved version of Matching Pursuit is proposed in the present work, which address the dispersion phenomena by decomposing a measured signal as delayed and dispersed impulse response of an atom, obtaining better features for Lamb wave measurements. The performance of the developed technique is tested for the approximation of real measured signals, showing its better performance compared to classical MPM, especially when dealing with dispersive signals. This improved signal decomposition can be very useful for SHM purposes.
A lift-off suppression algorithm to improve the accuracy of ACFM in crack detection of ferromagnetic materials
Authors:   Tianjin Special Equipment Inspection Institute, Guo Qi ,   Tianjin Special Equipment Inspection Institute, Wang Xuan ,   Tianjin Special Equipment Inspection Institute, Yu Haixu ,   Tianjin Special Equipment Inspection Institute, Du Fei ,   Tianjin Special Equipment Inspection Institute, Niu Weifei
Published: June 2024

Ferromagnetic pressurized components are widely used in petroleum, aerospace and nuclear industries, and their safety and stability are the key to the reliable operation of pressure equipment. In the process of crack detection of ferromagnetic materials by ACFM technology, the changes in lift-off distance caused by coating corrosion and irregularity of the surface will greatly affect the extraction of useful detection signals and reduce the detection accuracy. To overcome the drawback, a lift-off suppression algorithm for crack detection of ACFM is proposed. First, a differential strategy is proposed to discover the time-domain lift-off point of intersection (LOI) from the raw detection signals of ACFM for ferromagnetic materials. Then, the corresponding Bx and Bz signals are demodulated based on the amplitude of the LOI. The simulation and experimental results of the surface cracking of ferromagnetic materials by ACFM show that the novel Bx signals obtained according to the proposed method vary in a small range for different lift-off distances. The lift-off invariant property of the LOI enables the method to improve the interference of the lift-off effect on the conventional Bx signals and to enhance the detection accuracy of crack depth of ferromagnetic materials. The novel Bz signals perform consistently with the conventional Bz signals, and are virtually unaffected by the changes in lift-off distance.
The usage of interference effects in single Guided Ultrasonic Wave time signals for damage identification
Authors:   Technische Universität Braunschweig (TU), Rittmeier Liv ,   Faserinstitut Bremen (FIBRE), Polle Christoph ,   Technische Universität Braunschweig (TU), Sinapius Michael
Published: June 2024

Guided Ultrasonic Waves (GUW) interact with structural inhomogenities in multiple ways, e.g. mode conversions, mode scattering, oscillation phase shifts and reflections. These effects do occur at specimen edges and at damage on and in the specimen’s area. These effects at structural boundaries make the signal interpretation more complex. On the other hand they can lead to additional information in recorded time signals when caused by damage.
This study aims at the usage of interference effects in single time signals caused by damage in the structure. This is investigated changing the location of the measurements points in relation to the damage position. Two cases are taken into account: once the damage lies in the direct measurement patch between actuator and measurement point and once the damage lies outside the measurement path and its effect on the recorded time signal is investigated.
In the experimental setup, a rectangular Aluminum plate is used. Its dimension is chosen to make sure that edge reflections do not superimpose with the regarded wave packages within the time signals. A Laser-Doppler-Vibrometer allows the non-contact recording of several measurement points and is not wavelength-dependent as e.g. PZT-transducers. This allows the measurement to be unaffected by the transfer behavior of the transducers.
The relationship between the specific mode wavelength reflected by the damage and the measurement path length provides information about the expected level of interference at the considered measurement point.
The anticipation includes the indirect identification of damage off the direct measurement path through the observed interference effects, as well as variations in interference caused by the shift of the measurement point locations. The amplitude and phase changes serve as a measure of the interference and are obtained using the Hilbert transform.
Advancing Wireless Strain Sensing with Low-cost Printable Supersensitive Radiofrequency Patches
Authors:   King Abdullah University of Sience and Technology ((KAUST), MAHMOUD Hassan ,   King Abdullah University of Sience and Technology ((KAUST), Nesser Hussein ,   King Abdullah University of Sience and Technology ((KAUST), Lubineau Gilles
Published: June 2024

Engineering structures experience changes in material and functional properties due to structural damage, induced by sudden events or gradual degradation over time. Structural Health Monitoring (SHM) is crucial, utilizing real-time monitoring systems with various sensors. Suitable sensors for monitoring structures are vital for future economies. They should feature sensitivity, cost-effectiveness, easy integration, and reduced complexity. Current strain sensors, like strain gauges and Fiber-optic Brag Gratings (FBG), are sensitive but have drawbacks, including wiring complexities, installation time, and limited lifetimes. Introducing wireless sensors as a solution, enables autonomous strain monitoring, overcoming the drawbacks of traditional wired sensors. Chipless Radio Frequency (RF) technology, specifically passive RF sensors, stands out for smart structure development. These sensors, relying on RF readers for power, offer compactness, non-intrusiveness, longer lifetimes, and cost-effectiveness, making them promising for monitoring large structures.
This research aims to develop an innovative chipless RF strain sensor for wireless communication, emphasizing enhanced sensitivity and seamless integration into real structures for integrity monitoring. First, the structure and processing technique of such sensors will be presented. Second, an external readout coil connected to a vector network analyzer was used to make electromagnetic coupling with the receiver coil in the sensor and resonate the sensor on its resonant frequency. When experiencing strain, the signal activates a transmission line mechanism, increasing the attenuation along the capacitive element. The consequence is a strong variation in the effective sensor length making a remarkable variation in the resonant frequency.
We demonstrate the exceptional gauge factor in the resonant frequency at different strain levels, which gives our sensor a big advantage, compared to pure resistive sensors and geometrically related capacitive sensors. We also demonstrate that such sensors can be fabricated in large quantities at very low cost as they are by design compatible with classical printing technologies.
On-line Updated Guided Wave-Based Gaussian Process Method for Damage Evaluation of Aircraft Structure
Authors:   Nanjing University of Aeronautics and Astronautics  (NUAA), Tong Lin ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Jing Hutao ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Chen Jian ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Yuan Shenfang ,   Nanjing University of Aeronautics and Astronautics  (NUAA), Wang Hui
Published: June 2024

Accurate damage evaluation for an aircraft by using guided wave-based structural health monitoring (SHM) technology is in critical demand. An aircraft experiences various uncertainties during its service life, such as the time-varying environment, operational conditions, and different flight missions. Additionally, the actual aircraft structure involves complex structural forms such as varied thickness, stiffeners, ribs, and large cutouts, leading to a strong uncertainty of damage initiation and propagation. Consequently, when applied to an individual aircraft structure, the diagnosis model trained by prior data inevitably introduces errors, and the errors accumulate as the damage propagates, which brings challenges in engineering application. To achieve individual aircraft structure’s more accurate damage evaluation, an on-line updated guided wave-based Gaussian process (GP) damage evaluation method is investigated in this paper. Multi-source data from the design, service, and maintenance stages are utilized to continuously update and evolve the GP model, enabling its accurate evaluation throughout the long-term service life.
Instrumentation of the Eiffel Tower for Continuous Monitoring
Authors:   SITES, Manzini Nicolas ,   SITES, Brossault Marc-Antoine ,   SITES, Collard Aurèle
Published: June 2024

The Eiffel Tower stands as a historic and iconic monument in the city of Paris. With a total height of 330 meters and constructed with wrought iron, it features a lattice metal structure. Completed in 1889 after two years of construction, the tower was initially designed for a 20-year lifespan. Now over 130 years old and welcoming millions of visitors annually, it has become a compelling example of extending the lifespan of a structure through vigilant surveillance and maintenance.
Historically, the Eiffel Tower's surveillance program primarily consisted of repeated comprehensive and detailed inspections. Since 2021, it has been equipped with sensors to continuously monitor its overall behaviour. This instrumentation includes motoring of the spire’s inclination, as well as monitoring displacements, deformations, and vibrations in its four pillars. Temperature and humidity measurements are also acquired across the structure, to help providing a comprehensive analysis.
This paper presents the implemented instrumentation, addressing challenges related to its deployment, and proposes a first analysis two years after the beginning of the campaign. Seasonal movements and trends are identified, and a brief dynamic study is conducted. Finally, the paper discusses the approach of instrumentation for exceptional structures to complete ongoing surveillance.
Health Management of Aircraft Fuel Systems: A Practical Prognostic Perspective
Authors:   Cranfield University, Fu Shuai ,   Cranfield University, Avdelidis Nicolas Peter
Published: June 2024

Aircraft health management, specifically pertaining to fuel systems, is a comprehensive process that is undertaken to ensure the continuous airworthiness of the aircraft over its entire lifespan. The integration of health management into aircraft systems requires a methodical strategy to quantitatively evaluate the possible advantages provided by each specific application. Gaining a thorough comprehension of the advantages linked to integrated health management systems provides useful insights for enhancing the design of these systems and formulating efficient prognostic techniques. The role of prognosis in the field of prognostics and health management (PHM) is essential for improving the dependability of systems. Prognosis involves accurately predicting the remaining useful life (RUL) within the domain of integrated health management. This study improves the precision of prediction results, optimises the derived prerequisites for health management in the specialised field of RUL prediction for aircraft fuel systems, provides a thorough understanding, and advances the grasp of the previously described requirements in a more effective way. Additionally, it presents the most suitable solutions in order to address these demands by utilising prognostic techniques. Furthermore, this study presents an analysis of the proposed hybrid prognostic approach in the setting of controlled laboratory conditions. The applications encompassed in this work consist of three main areas. Firstly, the evaluation of inspection interval modifications is conducted by taking into account load and environmental monitoring data. Secondly, design considerations for damage prognostics are incorporated into the analysis. Lastly, a design modification is presented as an example of how condition-based maintenance (CBM) can be facilitated. A methodology that enables the efficient and well-founded construction of a health management system, specifically designed for aircraft fuel systems, is presented. This methodology, together with its associated outcomes, presents a compelling advantage in terms of lowering life cycle expenses while simultaneously enhancing availability, dependability, and performance.
Weld Defect Inspection of Cylindrical Secondary Batteries Based on Active Thermographic Measurement with Thermal Magnification
Authors:   Korea Advanced Institute of Science and Technology (KAIST), Park J ,   Korea Advanced Institute of Science and Technology (KAIST), Shin S ,   Korea Advanced Institute of Science and Technology (KAIST), Sohn Hoon
Published: June 2024

This paper presents an active thermographic measurement-based inspection tech-nique for detecting weld defects in the battery cap region of cylindrical secondary batteries. As the proliferation of electric vehicles continues, safety issues related to secondary batteries have become prominent. In this study, a defect detection algorithm was developed by aligning visual images and active thermographic im-ages of the cylindrical secondary battery cap region. Lock-in amplitude images during the heating-cooling process using a laser were extracted to measure the difference in temperature patterns arising from internal structural variations. Ad-ditionally, to minimize the influence of surface conditions and temperature varia-tions other than laser-induced, a 10Hz periodic heating was employed, and ther-mal magnification in the heating frequency was achieved through data processing. This approach allowed the measurement of temperature changes induced by laser heating and cooling while minimizing the impact of surface conditions and tem-perature variations unrelated to the laser. A dataset of 1000 specimens was con-structed by aligning thermographic data with vision images acquired through this method. defective samples. Using this thermographic-vision alignment dataset, a defect detection algorithm was trained to achieve a more accurate and faster in-spection performance compared to existing non-destructive testing technologies.
Smart Clay Bricks enabling Diffuse Strain Monitoring in Masonry Buildings: Progresses at Full-scale
Authors:   University of Perugia, Meoni Andrea ,   University of Perugia, D'Alessandro Antonella ,   University of Perugia, Triana-Camacho Daniel A. ,   University of Perugia, Mattiacci Michele ,   University of Perugia, Ubertini Filippo
Published: June 2024

Strain monitoring in masonry buildings can provide useful information regarding the onset of structural damage during operating conditions. This is because when cracks develop on masonry components, i.e., clay bricks and mortar layers, induce discontinuities and redistributions in their original stress/strain configurations. Although the application of local strain measurements for Structural Health Monitoring (SHM) of masonry buildings can be effective in detecting anomalies and modifications in their structural response caused by crack development, achieving diffuse strain monitoring remains a challenge in practical SHM applications, nowadays. This is mainly because strain sensors currently available on the market present several practical limitations, including scalability issues, which often make their large-scale application to masonry buildings difficult. In recent years, for overcoming the drawbacks of traditional sensor systems, innovative smart clay bricks have been developed, able to measure deformation when incorporated into load-bearing masonry structures. In that sense, this paper presents the latest advancements and applications of this brand-new sensing technology for SHM of real-scale masonry buildings.
Physics-Informed Machine Learning for Structural Damage Diagnosis in Aluminium Plates
Authors:   Politecnico di Milano, Pinello Lucio ,   Politecnico di Milano, Lomazzi Luca ,   Politecnico di Milano, Ghellero Jacopo ,   Politecnico di Milano, Giglio Marco ,   Politecnico di Milano, Cadini Francesco
Published: June 2024

Damage diagnosis plays a crucial role in Structural Health Monitoring (SHM) by facilitating the identification, localization, and estimation of the extent of defects in structures. Lamb waves, known for their sensitivity to defects, are widely employed in SHM methods for thin-walled structures. Most of those traditional methods require extracting damage indices from Lamb wave signals. This operation involves substantial post-processing and implies that part of the diagnostic information is lost. To solve those limitations and improve the damage diagnosis accuracy, machine learning methods have recently been proposed in the literature. However, the reluctance of the industrial sector to adopt conventional black-box models due to their lack of explainability poses a challenge.
This study proposes a physics-informed machine-learning approach to address the limitations of standard black-box methods. Particularly, a Physics-Informed Neural Network (PINN) is implemented to predict the density of an aluminium plate based on measurements of plate displacements caused by Lamb wave excitation. This is made possible by the implementation of a specific loss function, which leverages physical knowledge in the form of the partial differential equation governing Lamb waves.
Predicting the plate density based on measured displacements eliminates the need for artificial damage indices, utilizing the density variation itself to detect and localize damage. Additionally, the outputs of the PINN, rooted in physics equations, offer enhanced explainability compared to standard black-box models. The versatility of this framework extends to predicting material properties distributions for components, and efforts will be directed towards adapting the method for composite materials, where the approach may pose additional challenges.
Distributed polymer optical fiber sensors using digital I-OFDR for geotechnical infrastructure health monitoring
Authors:   BAM Federal Institute for Materials Research and Testing, Wosniok Aleksander ,   BAM Federal Institute for Materials Research and Testing, Königsbauer Korbinian ,   FibrisTerre GmbH, Nöther Nils ,   GGB mbH - Gesellschaft für Geomechanik und BaumesstechnikGesellschaft für Geomechanik und Baumesstechnik, Färber Jan ,   GGB mbH - Gesellschaft für Geomechanik und BaumesstechnikGesellschaft für Geomechanik und Baumesstechnik, Schaller Maria-Barbara ,   BAM Federal Institute for Materials Research and Testing, Krebber Katerina
Published: June 2024

We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geotextiles which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geotextile-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements.
In order to reduce overall costs of the sensor system and achieve its robustness required for use on site, the standard vector network analyzer used by default was replaced by an integrated digital board for optical frequency domain analysis. This implementation showed a significant improvement in the dynamic range and sensitivity compared to the previous measurement system based on the conventional network analyzer.
The functionality of the entire sensor system was proven in the field test during the installation of two geomats with integrated PF-POFs embedded into a road embankment in a section of the B 91 federal road near Werschen in the greater Leipzig area. To monitor the installation-related changes in the condition of both sensor-based geomats several control measurements on the embedded PF-POFs and co-integrated reference glass fibers were carried as the construction works progressed. The measurements recorded in the field confirm the great potential of the selected PF-POF used as the distributed fiber optic strain sensor. The future management of the remaining challenges of industrial strain-coupled sensor integration can result in a successful market launch of the PF-POF-sensor integrated into geosynthetics. The sensor system is ready to be used for practical application.
Stochastic estimation of wake-induced loads in wind farms using conformalized graph neural networks
Authors:   ETH Zürich, Duthé Gregory ,   Vrije Universiteit Brussel (VUB), De Nolasco Santos Francisco ,   ETH Zürich, Chatzi Eleni
Published: June 2024

In the evolving landscape of sustainable energy, wind power has become a cornerstone in the transition towards renewable energy sources. With significant advancements in turbine technology and simulation methods, wind farms are now a cost-effective alternative to traditional power plants. Nevertheless, optimizing the performance and lifespan of wind turbines remains challenging, especially when it comes to predicting and managing the cumulative load on turbine structures. Wake effects, which result from the complex aerodynamic interplay between turbines, reduce energy efficiency and increase mechanical stress on turbine components. A precise assessment of wake-induced loads is therefore vital for estimating the Remaining Useful Lifetime (RUL) of turbines.

To address these challenges, we introduce a novel approach using Graph Neural Networks (GNNs) in combination with Conformal Predictors to model wind farms and evaluate wake-induced loads while estimating uncertainty. GNNs are particularly adept at capturing the complex interactions in a wind farm due to their ability to model graph-structured data. This enables a more accurate representation of the aerodynamic interactions between turbines. Alongside the GNN framework, we employ Conformal Predictors for uncertainty estimation. Conformal Predictors provide statistically valid prediction sets based on past data and minimal assumptions, allowing us to estimate uncertainty bounds with improved confidence. The fusion of GNNs with Conformal Predictors offers a robust framework for predicting turbine loads, while reliably quantifying the uncertainty associated with these predictions.