bluuebunny/crossref_metadata_2025 huggingface dataset:
A hierarchical Bayesian approach for parameter identification in structural deterioration models using probabilistic surrogate models
Authors:   National Technical University of Athens (NTUA), Silionis Nicholas E. ,   National Technical University of Athens (NTUA), Anyfantis Konstantinos N.
Published: June 2024

Most structural systems currently in operation are subjected to potentially multiple deterioration processes, for which different models exist to describe their evolution in time. The quantities of interest (QoIs) involved in them are often directly unobservable. However, advances in Structural Health Monitoring (SHM) have enabled gathering structural response data which can be used to indirectly make inferences on unobservable (state) quantities through Bayesian inference. Obtaining updated estimates of deterioration model parameters, given observed data, enables them to be used to predict future health states of interest for the structure, which constitutes a core tenet of condition-based maintenance (CBM). Key to obtaining robust parameter estimates is accounting for different uncertain quantities involved in the model that transforms unobservable quantities to observable ones, which in a Bayesian context is achieved through the likelihood function. This work presents an offline, flexible hierarchical framework to tackle this problem that employs probabilistic reduced order models (ROMs) in the construction of the likelihood function, that enable marginalizing out these uncertain quantities. The approximate likelihood function is then used within a typical Markov Chain Monte Carlo (MCMC) setting for parameter estimation. The framework is demonstrated in applications related to deterioration processes common to ship structures.
Load identification for fatigue life assessment in ship hull structural elements
Authors:   National Technical University of Athens (NTUA), Panagiotopoulos Sotiris ,   National Technical University of Athens (NTUA), Anyfantis Konstantinos N.
Published: June 2024

The harsh and uncertain operational profile that characterizes the service life of a ship vessel demands for developing robust methods able to monitor the degrada-tion of its structural integrity. Load recognition is a central part in condition moni-toring of ship hull structures and when realized both diagnostic and prognostic ca-pabilities will be enabled. In this work a structural health monitoring framework is proposed under the conceptual basis of digital twining. The present approach em-phasizes the inverse identification of loads acting on the ship’s hull by exploiting readings from strain sensors and is cast in a Bayesian setting. The loading profile is then applied to the numerical model of the subject vessel for the estimation of the stress field at critical areas prone to fatigue damage, i.e. hot spots. The pro-posed framework is evaluated by synthetic strain data derived from a finite ele-ment (FE) model of a containership. The results demonstrate the feasibility of the proposed approach for addressing the load identification problem and acts as a starting point for the development of a structural digital twin that will allow for monitoring fatigue accumulation in ship hull structures.
An Evaluation Framework for Deep Learning-Based Anomaly Detection in Structural Health Monitoring.
Authors:   Vrije Universiteit Brussel (VUB), Bel-Hadj Yacine ,   Vrije Universiteit Brussel (VUB), Weijtjens Wout ,   Vrije Universiteit Brussel (VUB), Devriendt Christof
Published: June 2024

The task of evaluating deep learning algorithms in the context of Structural Health Monitoring (SHM) for damage detection is made particularly challenging by the limited availability of empirical data from damaged structures. Making it impossible to assert whether the trained algorithm would be able to pick up changes due to (unseen) damage. This study puts forth a methodology that employs synthesized anomalies to advance our understanding of the specific conditions under which deep learning algorithms for anomaly detection are succesfull and when they prove to be insensitive to damage.

This research aims to develop a comprehensive deep learning model that utilizes raw data as its input, negating the need for specialized preprocessing or the development of anomaly indices that are constrained to specific types of anomalies. Central to our methodology is the introduction of simulated damage into the data set through various manipulations. This evaluation method could be generalized across diverse sensor types, such as accelerometer data (ACCs) and Fiber Bragg Sensors (FBGs).

As a case study, we focus on accelerometer data, utilizing Power Spectral Density (PSD) as the input for a deep learning-based anomaly detection algorithm. The study employs both attenuation and the addition of harmonics at varying levels and frequencies to mimic anomalies, thereby investigation the model's areas of sensitivity. To empirically validate the approach, an 8-degree-of-freedom simulated system is used, and environmental effect is modelled by a linear stiffness reduction across all degrees of freedom (DOFs). Structural damage is then simulated by altering the stiffness of a specific DOF.

Our results demonstrate a robust correlation between the model’s success in identifying these synthesized anomalies and its capability to detect actual structural damage. This correlation serves as a valuable guide for hyperparameter optimization.
Cost-benefit analysis for SHM systems based on minimum detectable parameter change
Authors:   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Marsili Francesca ,   Lund University, Iannacone Leandro ,   Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg, Kessler Sylvia
Published: June 2024

Value of information (VoI) analysis can be used to assess the benefit of monitoring the health of infrastructure such as bridges and waterway locks. Such information supports decision-making processes, guiding operators towards the best actions for the infrastructure. In recent studies, VoI analysis is integrated with the modeling of the temporal evolution of structural conditions, so that the value of Structural Health Monitoring (VoSHM) can be assessed over the life cycle of the infrastructure. However, none of these studies considers the ability of the measurement system to detect small changes and its impact on the assessment of the VoI.
This paper presents an approach to VoI assessment based on the concept of minimum change detectability and Bayesian linear filters. By exploiting a feature of the Kalman filter whereby posterior covariance is independent of observations, it is possible to predict the minimum shift in the mean value of a structural parameter for which its posterior density function would exceed a user-defined safety threshold. By constructing the decision tree and considering the cost of monitoring and maintenance, the value of detecting smaller changes can be estimated, which is the value of early change detection and performing preventive maintenance.
As a proof of concept, this approach is applied to assess the VoSHM for a navigation lock built with precast reinforced concrete elements, where the steel bars in the chamber are damaged by fatigue and corrosion. The VoSHM is compared with the VoI associated to other Non Destructive Tests.
The paper then discusses the implications of applying Bayesian linear filters in SHM for parameter change detection, the main one being the development of refined scales for structural condition assessment before any data from the changed state is available. Ultimately, the VoSHM is the benefit brought by adopting these refined scales in maintenance management.
Reliability Assessment of Fiber Optic Shape Sensing
Authors:   University of Bologna, Falcetelli Francesco ,   National Research Council, Rossi Leonardo ,   National Research Council, Bolognini Gabriele ,   University of Bologna, Di sante Raffaella
Published: June 2024

Fiber optic shape sensing is a promising technology capable of enriching the field of Structural Health Monitoring (SHM), with many potential applications that expand the uses of distributed optical fiber sensors. The operational principle is based on the simultaneous strain acquisition from several optical fiber cores deployed in a single sensing cable (or multicore fiber). The parallel strain traces of these cores can be combined to extract curvature and torsion distributions along the sensing cable, that can be used for the three-dimensional reconstruction of the underlying shape. This study evaluates the reliability of fiber optic shape sensing for damage detection through the use of a model-assisted probability of detection framework specifically developed to simulate a carbon fiber-reinforced polymers specimen under quasi-static loading monitored by distributed optical fiber sensors based on Rayleigh backscattering. Specifically, we compare the performance of a traditional distributed optical fiber strain sensor with the one of a hypothetical distributed shape sensing cable (or multicore fiber). To perform this comparison, we redefine the damage index definition, which is usually based on the measured strain values, to make it compatible with shape sensing, where it can be defined in terms of curvature and torsion. With these preliminary results, we aim to evaluate whether shape sensing technology can be employed for damage detection to detect damage-related deformation, whether we can obtain better results compared to traditional optical fiber sensor applications, and discuss how damage metrics need to be modified. In future activities, these aspects will be further investigated taking into account other case studies and shape sensing configurations.
Acoustic emission monitoring during elastic bending of cross laminated timber-steel composite beams
Authors:   Technische Hochschule Mittelhessen (THM), Manthei Gerd ,   Technische Hochschule Mittelhessen (THM), Bohn Dennis ,   Technische Hochschule Mittelhessen (THM), Böhm Noah ,   Technische Hochschule Mittelhessen (THM), Kühn Bertram ,   Technische Hochschule Mittelhessen (THM), Vogelsberg Achim
Published: June 2024

Reinforcement of cross laminated timber (CLT) floors by a composite design with steel girders can provide an innovative and sustainable alternative for highly stressable floor systems made of steel and concrete for spans exceeding 8 m. To make this construction method economical and resource efficient, a significant contribution of the CLT panel to the composite stiffness is necessary. One key aspect of the composite design is the formation of the shear-resistant connection between CLT panel and steel girder.
The effect of both composites on the bending stiffness of CLT-steel composite beams was investigated in large-scale 4-point bending tests with spans of 8.1 m and 10.6 m. To monitor the damage of the specimen using acoustic emission (AE), a network with 16 AE sensors were attached to the surface of the specimen in area of largest deflection and highest load. For this purpose broadband AE sensors with a measurement frequency of up to 200 kHz were used.
The ultimate failure of the specimen occurred at a maximum test load of approximately 197 kN. The highest deflection at this force was about 192 mm. During the tests, which lasted about 50 minutes, more than 8,500 AE events were detected. In this contribution, the relationship between the mechanical quantities and the AE activity is shown.
Parametric Neural Networks as Full-Field Surrogates for Material Model Calibration
Authors:   Technische Universität Braunschweig (TU), Shivalingappa Ganesh ,   Technische Universität Braunschweig (TU), Anton David ,   Technische Universität Braunschweig (TU), Wessels Henning
Published: June 2024

The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures and materials age during their service life. This, in turn, leads to a deterioration in both the reliability and quality of the structures. Knowing the current condition of the building structures can help prevent disasters and extend service life.

We developed a physics-informed neural network (PINN) [1] for the calibration of the linear-elastic material model from full-field displacement data measured by digital image correlation. In an offline-phase, the PINN is trained to learn a parameterized solution of the underlying parametric partial differential equation without the need for training data [2]. We demonstrate the ability of the parametric PINN to act as a surrogate in a least-squares based material model calibration. In order to quantify the uncertainty, we further use the parametric PINN with Markov Chain Monte Carlo based Bayesian inference. Even with artificially noisy data, the calibration produces good results for reasonable material parameter ranges. Especially in sampling based methods, parametric PINNs have the advantage that model evaluation is very cheap compared to, e.g., the Finite Element Method. Thus, information on the material condition can be provided in near real-time in the online-phase. Moreover, PINNs use a continuous ansatz and thereby avoid the need to interpolate sensor locations to the simulation domain.

In our ongoing work, we plan to apply the parametric PINN to more complex material models, such as those for elasto-plastic materials. We also investigate the extension of the proposed method to more complex geometries.

[1] M. Raissi et al.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378, 686-707 (2019).

[2] A. Beltrán-Pulido et al.: Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems, IEEE Transactions on Energy Conversion 37(4), 2678-2689 (2022).
Influence of stiffeners on acoustic emission monitoring of ship structures
Authors:   Delft University of Technology (TU Delft), Saccone Cecilia ,   Delft University of Technology (TU Delft), Pahlavan Lotfollah
Published: June 2024

Naval vessels are valuable assets that are expensive to build and maintain. Predictive maintenance is crucial to enhance efficiency and operability and to extend the service life of these structures. Fatigue is regarded as one of the main damage modes affecting the structural integrity of ship structures. Fatigue cracks can develop at the intersections of stiffeners and other structural elements, without being detected until they substantially grow, introducing a degree of uncertainty in the estimation of the fatigue damage.
Acoustic emission (AE) is a passive ultrasound method for the detection and localization of different types of damage. AE is sensitive to crack propagation and can cover large areas, making it suitable for structural health monitoring of ship hulls. However, for accurate fatigue crack detection via AE it is crucial to understand how ultrasound waves interact with structural members i.e. stiffeners and longitudinal frames. Dispersion, scattering, reflection, and multimodality have so far hindered the application of AE in this context.
This study aims to assess ultrasound wave behavior in ship structures in the presence of structural elements such as stiffeners and longitudinal/transversal frames. It investigates the ultrasound wave transmission dependence on source frequency and angle of incidence using spectral finite element (SEM) simulations and experimental measurements. The experimental setup includes a 10mm thick steel plate with a stiffener and a longitudinal beam (Figure 1). Pairs of sensors are placed on each side of the stiffener to record the incoming and transmitted waves, and to determine the transmission coefficient.
By combining the results of both simulation and experiments, an expression for the transmission coefficient as a function of different frequencies and angles is presented.
The results of this study can be used to maximize the coverage of AE monitoring systems and enhance their sensitivity for detection of fatigue cracks on ship structures.
Semi-analytical simulation of ultrasound wave propagation in large plate-like structures
Authors:   Katholieke Universiteit Leuven Campus Kulak (KULK), Mardanshahi Ali ,   Katholieke Universiteit Leuven Campus Kulak (KULK), Van Den Abeele Koen ,   Katholieke Universiteit Leuven Campus Kulak (KULK), Chronopoulos Dimitrios
Published: June 2024

The ultrasonic guided wave (UGW) has gained considerable popularity among NDE methods for damage detection and structural health monitoring (SHM) due to its high frequency and low attenuation. Lamb waves are reflected or diffracted by structure boundaries, discontinuities, and damages. It is possible to determine a structure's health, defects, and locations based on its UGW propagation characteristics. In this study, we propose an efficient modeling method for UGW propagation through damaged and scattering plates by combining the Modal Pencil method with Wave Finite Element (WFE) and Hybrid FE/WFE methods and verifying the results with a high-precision elastic finite element model (FEM). With this approach, the transducer-excited ultrasonic field can be modeled, as well as its steady-state and transient propagation, scattering, and reflections at defects and boundaries. Long-range propagation configurations can be efficiently addressed by this method because its computation efficiency is independent of propagation path lengths. As part of the case study, we study the propagation of Lamb waves through a joint metal thin-walled structure with embedded damage. In the case study, different sensor locations are used to validate the received UGW signals quantitatively. Our findings will contribute to the development of a digital twin for monitoring structural health by providing an efficient and robust approach to modeling ultrasound propagation in damaged and large plate-like structures.
Monitoring tendon breaks in concrete structures at different depths using distributed fiber optical sensors
Authors:   Ruhr University Bochum, Paul Aeneas ,   Ruhr University Bochum, Sanio David ,   Ruhr University Bochum, Mark Peter
Published: June 2024

Unannounced tendon breaks are a dreaded failure mechanism in concrete

bridges. The failure of single wires or tendons does not necessarily cause cracking of the concrete. Thus, the damage cannot be detected by visual inspection. But, in the case of tendons bonded in concrete, re-anchoring causes characteristic local strain fields: tensile strains occur between the crack edges while three-dimensional compressive strains develop in the area around. On the surface, these strains are small and depend, e.g., on the concrete stiffness, the depth of the tendon inside the member and the anchorage length.

Such strain fields can be measured by fiber optical sensors on the surface in two dimensional grids. By evaluating the backscatter of emitted light beams, minimal strain changes (uncertainty: ±1 µm/m) can be detected in quasi-continuous resolution (0.65 mm pitch). An experimental investigation on a prestressed concrete beam with bonded tendons is presented. Gradually, three tendons of 10.5 mm diameter were mechanically cut to simulate failure, while longitudinal strains were measured on a 2D grid at the surface. With a prestressing force of 64.5 kN and up to a depth of 25 cm breaks of the tendons could be detected. They caused strains about 10-15 µm/m at the surface at maximum.

For varying tendon positions, the effect of depth on the strain is investigated by two sided measurements. As the distance between the break and the concrete surface increases, the strain significantly decreases but remains detectable. In the future, the results enable deeper analyses of the most influential parameters. The final aim remains to precisely detect the position and number of failed wires or tendons just from the strain signal at the surface.
Time of Arrival Extraction for Impact Localisation on Composite Structures
Authors:   Imperial College London, Xiao Dong ,   Imperial College London, Sharif-Khodaei Zahra ,   Imperial College London, Aliabadi M. H.
Published: June 2024

Time of arrival (TOA) serves as a widely utilised metric for pinpointing impact locations on composite aircraft structures. Nevertheless, the effectiveness and reliability of existing TOA extraction methods across diverse impact scenarios remains unclear. This study aims to comprehensively assess the effectiveness of established TOA extraction methods for impacts on composite structures, employing both analytical and experimental approaches.
Monitoring Fatigue of Grouted Connections Using Autoencoders and In-Situ Fiber Optical Sensors
Authors:   Leibniz University Hannover, Römgens Niklas ,   Leibniz University Hannover, Abbassi Abderrahim ,   Leibniz University Hannover, Talahmeh Muawiya ,   Leibniz University Hannover, Grießmann Tanja ,   Leibniz University Hannover, Rolfes Raimund
Published: June 2024

This paper investigates a traditional autoencoder trained with time series data for unsupervised damage detection and localization. The results are validated using innovative fiber optical sensory inside the grouted connection of a monopile sub-structure. Sensors are installed due to a limited understanding of the degradation mechanisms in grouted connections under cyclic loading. In most experiments, the grouted connection has only been assessed by destructively examining the grouted connection after the appearance of a complete crack pattern.
To address these limitations, the signals of the innovative fiber optical sensory are used to gain insight into the crack development inside the grout. Further, the dynamic response of the structure is evaluated using an autoencoder to monitor the changes linked to the degradation process. The fiber sensors are placed within the grout between the diagonal shear keys, which has been identified as the compression strut failure from previous experiments. The cyclic load was repetitively increased to simulate the degradation process and induce structural change inside the grouted connection. After each testing phase, the structure was excited using a shaker, and the acceleration sensors were evaluated using the autoencoder. The results of the study show a high sensitivity of the residuals to minor structural changes that can be explained using the sensors within the grout. Additionally, the local stiffness reduction can be localized evaluating the autoencoder residuals.
The study presents the innovative sensory to follow the crack opening of the grout due to cyclic loading. Further, these local changes are detected and localized using an autoencoder with time series data in vibration-based SHM.
Acoustic emission monitoring of composite marine propeller blades using embedded piezoelectric sensors and hydrophones
Authors:   Delft University of Technology (TU Delft), Huijer Arnaud ,   Delft University of Technology (TU Delft), Kassapoglou Christos ,   Delft University of Technology (TU Delft), Pahlavan Lotfollah
Published: June 2024

The marine industry is increasingly considering the use of flexible composite marine propellers for their potential to reduce carbon emissions and underwater radiated noise. Given the early stage of development of flexible composite propellers, there are unknowns on their structural degradation. Structural health monitoring (SHM) can provide additional insight into the occurrence and propagation of degradation in these structures. A passive and lightweight method of SHM is the measurement and processing of acoustic emissions (AE) that are induced by different degradation mechanisms.

The current research investigates the measurement of AE signals in composite marine propeller blades using embedded piezoelectric sensors. A full-scale glass-fibre polymer composite propeller blade is suspended in a tank filled with artificial seawater. The propeller blade contains 24 embedded piezoelectric sensors that were installed between laminas during manufacturing. Additionally, the tank includes an array of hydrophones for validation of the results. AE signals are simulated on the blade using underwater pencil lead breaks. The measured AE signals are assessed for their amplitude and frequency content. The results demonstrate the feasibility of measuring AE signals in composite marine propeller blades using embedded piezoelectric sensors and hydrophones.
Application of multi-label convolutional neural networks on enhancement of reliability of classification, a case study on bearing fault diagnostics
Authors:   Fraunhofer Institute for Structural Durability and System Reliability LBF, Mostafavi Atabak ,   Fraunhofer Institute for Structural Durability and System Reliability LBF, Friedmann Andreas
Published: June 2024

In contrast to the mutli-class classification where one and only one class would be assigned to each instance, multi-label classification allows for more flexible classification and possibility of having multiple labels for instance. This property enables us to model the dependencies between the classes such as effect of one label on the probability of occurrence of the other label. Considering these connections between the classes can help us to enhance the reliability of classifier and handle the contradictory prediction. Much like how we can assign multiple classes to an instance, we can also have an instance that remains unlabeled. This class is typically referred to as the "neutral" label, allowing us to bluntly state that we lack sufficient information to label the instance with adequate certainty.
Leveraging these properties, a 1D convolutional neural network (CNN) has been proposed to classify common faults occurring in the bearings. Network has the capability to assign a Confidence level between 0 to 1 to each class independently. Later, a threshold has been set on each class to identify the instance labels if any. Subsequently, instances with contradictory predicted label have been flagged as undetermined and removed from the predicted result. thus, the remaining classified instances can be labeled with sufficient confident. In the end, the multi-label CNN model performance has been compared with equivalent multi-class CNN model. Technique has been evaluated using MAFAULDA open dataset which is composed of 1951 multivariate time-series acquired by a triaxial accelerometer, a microphone, and a tachometer using SpectraQuest’s Machinery Fault Simulator (MFS) experimentally simulating different bearing states including normal operation as well as inner and outer race bearing faults. A data reduction scheme has been performed on the dataset to make the diagnostic more challenging and evaluate the performance of CNN in more realistic condition with insufficient data.
Experimental vibration-based output-only damage localization of mechanical systems
Authors:   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Rohrer Maximilian ,   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Moeller Max ,   Hochschule für Technik, Wirtschaft und Kultur Leipzig (HTWK), Lenzen Armin
Published: June 2024

Vibration-based Structural Health Monitoring (SHM) is becoming increasingly widespread in a variety of engineering sectors as its variability enables diverse applications. It enables full-time assessment of the status of a structure, allowing for early maintenance and repair to prevent further damage or even failure. The presented study is part of the SPP100+ program, which has the objective of extending the usability of complex structures. This study presents an experimental investigation of the damage localization of mechanical structures while considering varying environmental and operational conditions (EOC). For this, a testing field was designed and applied, enabling one to study the effects of the varying conditions in a comparable way. The research is based on the approach to modify the state projection estimation error (SP2E) method to account for the influence of EOC. The proposed method utilizes output-only system identification and state space parameterization to estimate the current structural state. Stochastic subspace identification (SSI) and H_∞ -estimation are employed to identify the state space model during a learning phase. Subsequently, the SP2E method is applied during the monitoring phase to localize structural alterations. Moreover, an algorithm is introduced to estimate the weighting factors of different environmental and operational conditions. To verify the efficacy of the proposed method, an extensive experimental study is conducted. The numerical analysis successfully demonstrates the applicability of the method for localizing mass perturbation and stiffness degradation under various EOC. This work contributes to proactive maintenance and damage detection for mechanical structures under varying conditions.
Experimental Study Employing EMI-based Piezoelectric Diaphragms to Detect Water Leakages in PVC Pipes
Authors:   Birmingham City University, De Oliveira Mario ,   Cassioli Group, Pinto Rodrigo
Published: June 2024

Considering the increased demand for using natural resources sustainably, water is an ultra-valuable resource to be preserved. Notwithstanding, Polyvinyl Chloride (PVC) pipes, which are mainly used in complex building water distribution systems, are still in use and present a considerable rate of water loss. Unfortunately, the recently developed Structural Health Monitoring (SHM) has not deeply covered leaks in those pipes. To fill this gap, this approach proposes an experimental study to assess the feasibility of using Lead Zirconate Titanate (PZT) diaphragms to detect water leaks in these structures. Experimental tests were conducted by attaching one PZT diaphragm to a mini-scale water distribution system, where the leakage scenarios were simulated by opening taps. To attest to the effectiveness of the methodology, it calculated statistical metrics between the baseline (without leaks) and unknown conditions. The experimental results are promising and may be extrapolated to detect water losses in a full-scale water-distributing system.
Feature Engineering for Physics-informed Evaluation of Electromechanical Impedance Measurements for Sandwich Face Layer Debonding Identification
Authors:   Johannes Kepler University Linz (JKU), Kralovec Christoph ,   Silicon Austria Labs GmbH, Lehner Bernhard ,   Silicon Austria Labs GmbH, Huber Christian ,   Silicon Austria Labs GmbH, Gerling Konrad Santiago ,   Johannes Kepler University Linz (JKU), Schagerl Martin
Published: June 2024

The present research exploits various physically explainable features for the evaluation of electromechanical impedance (EMI) measurements for sandwich face layer debonding identification using a recently published two-step physics- and machine-learning (ML)-based approach.
In the previous work, the ML approach used employs a One-Class Support Vector Machine and a K-Nearest Neighbor model for debonding detection and size estimation, respectively. Feature engineering was used to compensate for the simplifications of the finite element (FE)-based physical model and a simple data calibration step was used to adapt to distribution shifts. This enabled to train both ML models exclusively with FE simulation-based synthetic EMI spectra data. The efficacy of the method was demonstrated on real-world EMI spectra measurements of a circular aluminum sandwich panel by a piezoelectric transducer. The considered face layer debonding damage was idealized and stepwise increased by a milling process.
In the present work, we explore opportunities for further optimization of our method with respect to data distillation, where we reduce the computational requirements of both training and inference while at the same time preserving essential information. Furthermore, we investigate the usefulness of a separate approach for debonding detection, formulated as an anomaly detection problem.
The data preprocessing and ML models are validated by unseen real-world EMI measurement data and benchmarked with the previously published damage evaluation results, considering both reliability and accuracy.
The data and the code used in this study are provided in their entirety to enable reproducibility, enhance comprehensibility, and encourage future research.
The initial structural health monitoring system of the Nibelungen Bridge Worms
Authors:   Dresden University of Technology (TU Dresden), Eisermann Cedric ,   MKP GmbH, Voigt Chris ,   Dresden University of Technology (TU Dresden), Marx Steffen ,   Dresden University of Technology (TU Dresden), Kang Chongjie
Published: June 2024

As an essential part of the validation project for the German Research Foundation-funded Priority Program SPP 100+, an initial structural health monitoring system has been implemented on the Nibelungen Bridge in Worms. The aim of this system is to monitor the general behavior of the bridge for further development of condition indicators and to provide real data to all project partners to validate their methods. In this scope, 20 sensors, including radiation, humidity, temperature, inclination, linear variable differential transformer (LVDT), and acceleration sensors, have already been installed on the bridge. The quality of the recorded data, including measurement technical traceability, completeness, and reliability, shall be guaranteed. Subsequently, both the raw data and the first statistically evaluated values, such as the maximum, average, and minimum values, are saved. These raw and first-hand data are made available to the involved partners in a possible real-time manner. In addition, a web-based visualization platform has also been established. Therefore, users can access the deviation of the measured values from different sensors in the most direct way. The measured results of this initiated SHM system are compared and validated with numerical simulation results. Further supplementation of additional sensors, extension, and improvement of this initial system are aimed to be achieved in the future.
Guided Ultrasonic Wave Monitoring of Damage in Anisotropic Composites
Authors:   University College London (UCL), Hervin Flora ,   University College London (UCL), Fromme Paul
Published: June 2024

Lightweight, carbon fibre reinforced composites are often employed for the manufacture of aerospace components due to their good strength to weight ratio. However, due to poor interlaminar strength, composite components are prone to barely visible impact damage, caused by low velocity impacts, during aircraft operation. Guided-wave-based structural health monitoring (SHM) techniques, using a network of distributed sensors, is an important Structural Health Monitoring (SHM) tool for the detection and localization of in-service impact damage in composite structures. However, the material anisotropy of composite laminates influences guided wave propagation and scattering. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Defect characterization can be improved by considering the scattering characteristics of various damage types for the sparse array signal processing.

Guided wave scattering (A0 Lamb wave mode) was investigated around an artificial insert delamination in a quasi-isotropic CFRP panel. Permanent magnets, mounted on an undamaged region of the plate, were also used as scattering targets and compared to the delamination case. Full 3D Finite Element (FE) simulations were performed for both the delamination and magnet cases and compared to wavefield data obtained from non-contact laser Doppler vibrometer (LDV) measurements. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. Good agreement was found between the experimental measurements and simulations. Scattered guided wave amplitudes around each damage type show strong directional dependency with energy focusing along the fiber directions of the outer ply layers of the laminate. Distinct scattering behavior was observed for each damage type. Implications of anisotropy and angular scattering on sparse array SHM of different defect types are discussed.
Shape Sensing of Resin Pipe with a Helical-winding Optical Fiber in Bending Test
Authors:   University of Tokyo, Uchida Mami ,   University of Tokyo, Zhang Tianjun ,   University of Tokyo, Kobayashi Makito ,   Kajima Corporation, Fujiwara Kotaro ,   Kajima Corporation, Imai Michio ,   Kajima Corporation, Nagatani Hideki ,   Kajima Corporation, Miyajima Yasuyuki ,   Kajima Corporation, Kawabata Junichi ,   University of Tokyo, Murayama Hideaki
Published: June 2024

In this study, we developed a shape-sensing technique for a pipe. An optical fiber was helically bonded on the outer surface of a resin pipe whose length, outer diameter and inner diameter are 3200 mm, 34 mm, and 26.6 mm, respectively. Firstly, four grooves were made on the surface along the pipe in a helical pattern with the same pitch of 360 mm. At the one pipe end, adjacent grooves were offset by 90 degrees and the offset was maintained along the pipe. The single-mode fiber was put into a groove and fixed by epoxy and then it was embedded into the adjacent groove. Consequently, the optical fiber was going back and forth twice along the pipe. At the pipe ends, there were stress-free fibers between the grooves. Shape sensing for the pipe is conducted as follows.
1. Strain distributions along the grooves are measured by a distributed fiber-optic strain sensing system.
2. The bending strain is extracted from the measured strain to calculate the curvature and bending angle.
3. The shape of the pipe is reconstructed based on the curvature and bending angle using the Frenet-Serret formulas.
Three-point bending tests were conducted. In the tests, the pipe ends were clamped to be the fixed ends and a point load was applied to the pipe center. Strain distributions were measured by tunable wavelength coherent optical time domain reflectometry (TW-COTDR) or optical frequency domain reflectometry (OFDR). Then the pipe shape was reconstructed as described above. We compared the reconstructed shape with the deformation calculated by finite element analysis and the agreement was excellent.
If we insert the developed pipe into a bedrock and reconstruct the pipe shape continuously, it is expected that we can detect cracks or damage in the bedrock. This approach is expected to make rock excavation constructions safer.
Transfer learning applications for corrosion prediction for electrodissoluted specimens
Authors:   Politecnico di Milano, Pinello Lucio ,   Politecnico di Milano, Panagiotopoulou Vasiliki ,   Politecnico di Milano, Giglio Marco ,   National Research Council, Institute of Condensed Matter Chemistry and Energy Technologies (CNR-ICMATE), Vazquez Gomez Lourdes ,   National Research Council, Institute of Condensed Matter Chemistry and Energy Technologies (CNR-ICMATE), Mattarozzi Luca ,   Politecnico di Milano, Sbarufatti Claudio
Published: June 2024

Corrosion monitoring is a critical aspect of Structural Health Monitoring (SHM), as it presents inherent challenges in tracking its progression and requires strategically placed sensors for effective surveillance. Moreover, the intricate interplay between environmental factors and the material composition of corroding components further complicates this phenomenon.

In this study, a novel neural network framework is designed to predict corrosion evolution based on environmental data, focusing on three distinct aluminium alloys: Al7475, Al2024, and F357. A dedicated neural network model is developed for Al7475, trained on electrodissolution data, including process parameters and profilometry measurements. This model predicts corrosion progression in terms of volume loss, corroded depth, and area based on charge measurements. To address the challenge of material-environment coupling, transfer learning is employed to adapt the neural network to Al2024 and F357.

The oxidation charge passed during the electrodissolution mimics in an artificial way the corrosion process due to environmental conditions in the attack site. Consequently, the implemented neural network establishes a robust connection between corrosion process measurements and profilometry data, enabling accurate corrosion progression prediction based on oxidation charge. This framework allows the prediction of a component's residual useful life based on the assessment of the corrosion state, with the knowledge of volume loss further supporting mechanical assessment by evaluating resistant section or pit dimensions, ultimately aiding in the pit-crack transition analysis.
Detecting wire breaks in post-tensioned tendons of wind turbines: A signal energy spectrum analysis approach
Authors:   Dresden University of Technology (TU Dresden), Xu Ronghua ,   Leibniz University Hannover, Lange Alexander ,   MKP GmbH, Käding Max ,   Leibniz University Hannover, Ostermann Jörn ,   Dresden University of Technology (TU Dresden), Marx Steffen
Published: June 2024

In the wind industry, recent advancements have led to the research and development of wind turbines featuring hybrid towers, a novel combination of steel tube and precast concrete segments. These concrete modules, which absorb the high stresses caused at the intersection between the tower and its foundation, are commonly joined with post-tensioned tendons. This study focuses on the identification of wire breaks in post-tensioned tendons through the application of acoustic emission testing. Measurements involve the evaluation of wire breaks under controlled laboratory conditions and the analysis of the dynamic operational environment of a functioning wind turbine. The investigation encompasses the development and assessment of a methodology based on principles of discrete Fourier transformation and Parseval’s theorem for analyzing the frequency-dependent energy distribution of acoustic signals. This analytical approach extends to the examination of wire breaks generated under varying stress levels as well as the operational noise within the actual wind turbine. The findings demonstrate a notable concentration of energy within the frequency range of 5-20 kHz in the wire break signals. In contrast, the operational noise signals show a distinct pattern, presenting an extreme value distribution within the 0-2 kHz range. The developed methodology exhibits robustness, even in the presence of high background noise levels, establishing itself as a valuable tool for wire break detection. Additionally, its potential for integration with machine learning algorithms offers the prospect of automated wire break detection, enabling further advancements in structural health monitoring for wind turbines.
Integrating Model-Centric and Data-Centric Techniques for Pipe System Prognostics and Health Management
Authors:   Ecole centrale de Lyon, Braydi Ahmad ,   Ecole centrale de Lyon, Fossat Pascal ,   Ecole centrale de Lyon, Casaburo Alessandro ,   Ecole centrale de Lyon, Pernet Victore ,   Ecole centrale de Lyon, Zwick Cyril ,   Ecole centrale de Lyon, Ardabilian Mohsen ,   Ecole centrale de Lyon, Bareille Olivier
Published: June 2024

In today's industrial landscape, the proactive implementation of predictive maintenance techniques is imperative, especially in the context of pipe systems, as companies increasingly embrace cutting-edge technologies such as artificial intelligence and the Internet of Things (IoT). Orano/La Hague, like many other industry leaders, recognizes the vital importance of integrating these technological advancements into their operations. One of the critical challenges they face relates to recurrent pipe-clogging incidents, leading to energy inefficiencies and financial losses. Addressing maintenance needs proactively is essential to mitigate risks and ensure the safety of both personnel and valuable assets.
This research addresses these challenges by introducing an innovative hybrid approach that combines data-centric and model-centric methodologies for the continuous prognostics and monitoring of pipeline systems. Leveraging experimental passive acceleration measurements, this approach offers a reliable means to predict and assess the severity of clogs as they occur. To enhance the accuracy of predictions, a sliding window technique is employed to minimize noise and extract pertinent features from the data. The results of this study highlight the exceptional effectiveness of the proposed approach in accurately predicting clogging incidents and quantifying their severity, even in scenarios involving varying airflow rates within the pipes.
This research marks a significant step forward in the domain of prognostics and health monitoring, with the potential for widespread applications across various industries. The integration of data-centric and model-centric approaches represents a promising solution to the complex challenge of predicting and preventing pipe-clogging incidents, ultimately contributing to enhanced operational efficiency and asset protection
Electromechanical Impedance of Acoustic Emission Sensors used for Self-Diagnosis
Authors:   Bochum University of Applied Sciences (BO), Priebe Sebastian ,   Bochum University of Applied Sciences (BO), Reinhardt Christopher ,   University of Augsburg, Fernandes Adelmo ,   Vallen-Systeme GmbH, Trattnig Horst ,   University of Augsburg, Sause Markus G. R. ,   Bochum University of Applied Sciences (BO), Mueller Inka
Published: June 2024

Acoustic Emission (AE) is a well established NDT method which is used more and more for continuous monitoring, heading towards SHM. Today, AE sensors in the field are tested with a pencil lead break test (Hsu-Nielsen source), which requires manual interaction. Sensors are replaced if the sensitivity is too low. However, since the test is performed on the monitored structure, the structure itself may influence the test. Therefore, a detailed check of the sensor in the lab, according to SE02 and ISO 24543, is done afterwards. An automated solution, that can test AE sensors in the field, without human interaction, that needs minimal additional hardware would benefit AE based monitoring systems.

The electromechanical impedance (EMI) spectrum is mostly used for local damage detection with piezoelectric transducers. In the past it has been shown that the EMI spectrum can also be used to enable automated self-diagnostic capabilities in the low frequency spectrum (Park et al., 2004) and in the higher frequency regime (Mueller, 2017) including the eigenfrequencies of the transducers. The usage of EMI for AE sensors can, thorough research in this matter provided, solve the issues mentioned above.

This work presents results of EMI spectrum analysis of two sets of 12 and 17 sensors, respectively, with different levels of damage from pressure vessel burst tests. The results are compared to that of a face-2-face inspection, which is an established method of verification in the lab according to ISO 24543. It is shown that the EMI spectrum is capable of similar self-diagnostic capabilities. Thus, combining the advantages of global monitoring with AE sensors, while providing a remote and automatic method of self-diagnostic of AE sensors at any point with minimal additional hardware and software requirements.