Tension members in major civil and building structures such as bridges, railways, and offshore structures serve as critical components for load transmission and structural stability assurance. However, long-term use, environmental exposure, and inadequate maintenance can lead to anomalies in tension force, resulting in an increasing trend of structural collapse incidents worldwide. This study aims to prevent such failures by developing an openable detachable split Yoke-type Elasto-Magnetic (E/M) sensor and verifying its performance for monitoring tension force in tension members. Previous Yoke-type E/M sensors were designed with only the primary excitation coil featuring an openable structure, while the secondary sensing coil had a non-openable structure, which necessitated on-site winding and limited practical field applicability. In this study, both the primary and secondary coils were designed with openable structures. COMSOL Multiphysics 3D simulations were performed to validate the design. The magnetic flux density of the sensor was measured using the Magnetic Fields module to verify magnetization characteristics, and based on these validation results, the sensor was fabricated through precision coil winding. To evaluate the sensor performance, induced voltage signals were acquired at 1-ton increments from 0 to 10 tons in both the COMSOL Multiphysics simulation environment and simultaneous physical experiments. The peak-to-peak voltage values were obtained by calculating the difference between the maximum and minimum values of the collected induced voltage signals. A regression analysis was then performed on the average peak-to-peak values to derive a quantitative tension force estimation formula. The derived formula was validated through comparison of simulation results with experimental results. Additionally, to enable future application to floating offshore wind turbine mooring lines, underwater environment simulation was performed based on electrical properties reflecting actual seawater conditions. Sensor performance variability according to external environment was analyzed, and practical field applicability was verified. This study advances sensor technology for structural health monitoring by proposing the design and fabrication of a fully openable, detachable split yoke-type E/M sensor and by experimentally and numerically establishing a quantitative relationship between induced voltage signals and tension force, thereby strengthening the academic foundation of non-destructive evaluation technology.

The Chain is discontinuous structures component widely used as critical components for power transmission and load bearing in various industrial facilities. However, local damage caused by repeated loading, friction, corrosion, and fatigue cracking can lead to catastrophic chain failure and unexpected equipment shutdown, resulting in severe safety risks. Accordingly, the objective of this research is to prevent chain failure-induced accidents by designing a Magnetic Flux Leakage (MFL) sensing system and experimentally verifying its capability to detect early-stage localized damage. To achieve this, an MFL sensor was designed to detect local damage non-destructively. A magnetization unit consisting of Neodymium permanent magnets and carbon steel yokes was applied to magnetically saturate the chain, and an array of 8-channel Hall sensors (positioned on the top and bottom) measured the leakage flux signals generated from the damaged areas. An experimental chain specimen of 3 m length with artificially machined defect depths between 1–4 mm is prepared, and leakage-flux measurement is repeated 400 times per damage level using a DAQ board for high-speed sequence acquisition. Because the acquired leakage sequence includes electrical noise and magnetization-induced baseline drift, a moving-average-based detrending method is applied to suppress non-diagnostic low-frequency components while preserving defect-related modulation. Fast Fourier Transform (FFT) analysis is performed to project the spatial magnetic leakage pattern into the frequency domain. Damage-governed spectral peaks that grow distinctly with defect severity emerge within constrained frequency bands, and these peaks are designated as core defect-signature frequency components. A band-pass filter retains only these core components, and Inverse FFT (IFFT) reconstructs them back to the spatial distance domain for direct visual confirmation of defect localization and severity-dependent amplitude variation. This study is expected to enhance the efficiency and reliability of chain maintenance and diagnosis through the design, fabrication, and verification of the MFL sensor. Consequently, this research contributes to the advancement of the Structural Health Monitoring (SHM) field.

Mobility is undergoing increasing electrification, driven not only by the rise of electric vehicles but also by the rapid expansion of micro-mobility. The latter, comprising bikes, e-scooters and electric scooters used privately or in shared-mobility fleets, primarily uses battery packs composed of cylindrical lithium-ion cells. During operational use, these batteries experience significant mechanical stresses: vibrations caused by road conditions, repeated shocks, rough handling, and occasional drops. Such constraints can compromise battery-pack integrity and pose safety risks to both people and property.

Among the critical elements to monitor are the welds that interconnect cells in series or parallel. A degraded weld can lead to a short circuit or a local increase in resistance, potentially resulting in a hot spot. Early detection of such defects is therefore essential to ensure the safety and reliability of the battery system.

This study introduces an inspection method based on Time-Domain Reflectometry (TDR), enabling fast and reliable detection of faulty welds on cylindrical cells. Comparative measurements between weld resistance obtained with an ohmmeter and TDR signatures demonstrates the capability of this technique to identify degraded welds. Furthermore, the repetitive structure of lithium-ion cells within a module provides a characteristic pattern that allows the detection of local anomalies inside battery pack.

The proposed solution, both innovative and easily integrable, paves the way for accurate, low-cost weld diagnostics in cylindrical-cell battery packs, thereby enhancing the safety of electrical mobility systems.

The knowledge of the profiles of concrete water content is very important regarding the durability prediction of civil engineering reinforced concrete structures. To monitor changes in water content gradients in these structures, complementary ultrasonic, capacitive and electrical sensor systems were designed in the framework of the ANR-SCaNING research project. Eight common, useful depths were defined in order to obtain measurements in the same areas, in the cover concrete of the rebars and in the structure concrete core. The ultrasonic sensor system consists of eight pairs of transducers. The initial observable is the propagation velocity of the compression wave, while the observable monitored over time is the relative velocity variation [1]. The capacitive system consists of a double ladder with eight electrodes each. The observable is the dielectric constant which is the real part of the relative permittivity [2]. Finally, the resistive sensor consists of a multi-electrode ladder that can be interrogated in Wenner configuration or transmission configuration [3]. The observable is the electrical resistivity. These systems were embedded in a 100x100x30 cm concrete slab that was submitted to a three-month unidirectional drying in an oven at T=45°C. The gradients of the various observables are monitored over time and can be processed to obtain water content profiles.

The aim of this paper is to present the three different embedded sensor systems and to explain the processing approaches. The results will show the evolution of the gradients of the observables over time. Finally, the water content profiles obtained after three months of drying will be compared for the three sensor systems.

[1] Hariri R., Chaix J.-F., Shokouhi, P., Garnier, V., Saïdi-Muret, C., Durand, O., Abraham, O. (2024) Quantifcation of the Uncertainty in Ultrasonic Wave Speed in Concrete: Application to Temperature Monitoring with Embedded Transducers. Sensors 2024, 24, 5588. https://doi.org/10.3390/s24175588

[2] H. Ibrahim, G. Villain, N. Ranaivomanana, S. Palma Lopes, J.-P. Balayssac, T. Devie, X. Dérobert. (2024) Design and validation of a multi-electrode embedded capacitive sensor to monitor the electromagnetic properties in concrete structures, Measurement, Vol. 236, 2024, 115057. https://doi.org/10.1016/j.measurement.2024.115057

[3] Badr J., Fargier Y., Palma-Lopes S., Deby F., Balayssac JP., Delepine-Lesoille S., Cottineau LM., Villain G. (2019) Design and Validation of a Multi-Electrode Embedded Sensor to Monitor Resistivity Profiles over Depth in Concrete, Construction and Building Materials, Vol. 223, 30 October 2019, pp. 310-321. https://doi.org/10.1016/j.conbuildmat.2019.06.226

Fatigue in metallic materials leads to progressive degradation driven by a sequence of microstructural mechanisms occurring over the life cycle. While fracture is typically the most obvious and critical damage state, it only appears at the end of life. However, when no fracture is monitored a material’s degree of fatigue degradation becomes difficult to be quantified. During that period a variety of other mechanisms act in a consecutive way starting from dislocation movements, phase transformation and leading over to plastic deformation. Electromagnetic techniques such as eddy current testing (ECT) are an interesting option to be considered for monitoring ferromagnetic materials. The attraction is the complexity of these ECT signals and the question arises: What can be read out of those?

To address this question, fatigue experiments were conducted on structural steel S235 (ASTM A36) under strain-controlled constant-amplitude loading to determine what is generally known as an S-N curve. All tests were continuously monitored using a commercial ECT system. The signals recorded were processed in terms of the real and imaginary part and the resultant impedance and phase were determined as further parameters characterizing ECT. These were then plotted in a specific 3D format displaying loading over normalized life (in the sense of the traditional S-N curves) added by one of the forementioned ECT parameters as the third dimension. The resulting topography is shown as an example for ECT impedance in Fig. 1. Such a visualization allows the monitoring capability of the respective technique (here ECT impedance) to be discussed.

Beyond this visualization, two complementary statistical processing frameworks were employed to analyze the time-domain signal response recorded. First, statistical thresholding techniques—both linear and moving standard-deviation based—were applied to identify significant deviation points that might be correlated with transitions in the material state. Second, a weighted scoring method was introduced leveraging nine time- and frequency-domain features including slope, roughness, RMS, high/low frequency ratio, local variance, kurtosis, zero-crossing rate, envelope rate, and the acceleration of the second-order slope. Robust normalization schemes were incorporated to reduce noise sensitivity and enhance tracking stability. Initial findings have been analyzed regarding these statistical approaches to possibly highlight critical transitions during the fatigue life and exhibit sensitivity to early state changes. The weighted scoring approach in particular shows improved stability compared to single-parameter tracking, making it a possible candidate for future automation.

In the broader perspective, this work establishes a data-driven electromagnetic monitoring framework that with possible potential to support future machine learning-based damage classification even to be used in structural health monitoring. Ongoing work focuses on refining feature selection, enhancing thresholding robustness, and evaluating performance across multiple material grades and loading conditions.