Magnetic Sensor Array Research Articles

Design and implementation of a nondestructive testing system for magnetic field imaging based on machine learning

This study presents the design of a nondestructive testing (NDT) system for detecting metal defects using a magnetic field sensor array. The system integrates a hardware-software detection framework that includes parallel computation and processing cores, along with multiple anisotropic magnetoresistance (AMR) sensors. These AMR sensors capture subtle variations in the magnetic field, which serve as the foundation for defect identification. The system enables synchronized data acquisition from multiple AMR sensors, achieving multi-axis data fusion and parallel signal processing. Furthermore, the detection system gathers defect features to train an NDT system based on machine learning (ML). Subsequently, ML-generated defect imaging features are processed through an imaging framework, producing magnetic field imaging (MFI) for precise defect localization. The study tested six different metal samples with various defect sizes and types to validate the system. The results demonstrate the system’s ability to accurately detect and identify different defects, highlighting the high precision of magnetic field detection and the overall effectiveness of the proposed system for NDT.

Magnetic augmentation through multi-gradient coupling enables direct and programmable profiling of circulating biomarkers

Conventional magnetic biosensing technologies have reduced analytical capacity for magnetic field dimensionality and require extensive sample processing. To address these challenges, we spatially engineer 3D magnetic response gradients for direct and programmable molecular detection in native biofluids. Named magnetic augmentation through triple-gradient coupling for high-performance detection (MATCH), the technology comprises gradient-distributed magnetic nanoparticles encapsulated within responsive hydrogel pillars and suspended above a magnetic sensor array. This configuration enables multi-gradient matching to achieve optimal magnetic activation, response and transduction, respectively. Through focused activation by target biomarkers, the platform preferentially releases sensor-proximal nanoparticles, generating response gradients that complement the sensor’s intrinsic detection capability. By implementing an upstream module that recognizes different biomarkers and releases universal activation molecules, the technology achieves programmable detection of various circulating biomarkers in native plasma. It bypasses conventional magnetic labeling, completes in <60 minutes and achieves sensitive detection (down to 10 RNA and 1000 protein copies). We apply the MATCH to measure RNAs and proteins directly in patient plasma, achieving accurate cancer classification.

Magnetic Sensor Array for Electric Arc Reconstruction in Circuit Breakers.

Noninvasive imaging of circuit breakers under short-circuit testing is addressed by recording the magnetic field produced over an array of external sensors and by solving an inverse problem to identify the causing current distribution. The temporal and spatial resolution of the sensing chain are studied and implemented in a physical set-up. A wire model is adopted to describe electrical current distribution. Additionally, the simpler, more direct approach to evaluating the passage of electric current in front of sensors is proposed. The dynamics of suitable approximating models of the electric arc that forms across contacts is obtained and agrees with multi-physical simulations and with experimental time histories of current and voltage. The two methods are flexible and allow the analysis of different types of circuit breakers.

An ultrathin, rapidly fabricated, flexible giant magnetoresistive electronic skin

In recent years, there has been a significant increase in the prevalence of electronic wearables, among which flexible magnetoelectronic skin has emerged as a key component. This technology is part of the rapidly progressing field of flexible wearable electronics, which has facilitated a new human perceptual development known as the magnetic sense. However, the magnetoelectronic skin is limited due to its low sensitivity and substantial field limitations as a wearable electronic device for sensing minor magnetic fields. Additionally, achieving efficient and non-destructive delamination in flexible magnetic sensors remains a significant challenge, hindering their development. In this study, we demonstrate a novel magnetoelectronic touchless interactive device that utilizes a flexible giant magnetoresistive sensor array. The flexible magnetic sensor array was developed through an electrochemical delamination process, and the resultant ultra-thin flexible electronic system possessed both ultra-thin and non-destructive characteristics. The flexible magnetic sensor is capable of achieving a bending angle of up to 90 degrees, maintaining its performance integrity even after multiple repetitive bending cycles. Our study also provides demonstrations of non-contact interaction and pressure sensing. This research is anticipated to significantly contribute to the advancement of high-performance flexible magnetic sensors and catalyze the development of more sophisticated magnetic electronic skins.

High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers with 2D Oscillator Array and Inter-Sensor Spectrogram Cross-correlation.

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

Mitigation of Device Heterogeneity in Graphene Hall Sensor Arrays Using Per-Element Backgate Tuning.

Graphene Hall-effect magnetic field sensors (GHSs) exhibit high performance comparable to state-of-the-art commercial Hall sensors made from III-V semiconductors. Graphene is also amenable to CMOS-compatible fabrication processes, making GHSs attractive candidates for implementing magnetic sensor arrays for imaging fields in biosensing and scanning probe applications. However, their practical appeal is limited by response heterogeneity and drift, arising from the high sensitivity of two-dimensional (2D) materials to local device imperfections. To address this challenge, we designed a GHS array in which an individual backgate is added to each GHS, allowing the carrier density of each sensor to be electrostatically tuned independent of other sensors in the array. Compared to the constraints encountered when all devices are tuned with the same backgate, we expected that the flexibility afforded by individual tuning would allow for the array's sensitivity, uniformity, and reconfigurability to be enhanced. We fabricated an array of 16 GHSs, each with its own backgate terminal, and characterized the ability to modulate GHS carrier density and Hall sensitivity within CMOS-compatible voltage ranges. We then demonstrated that individual device tuning can be used to break the trade-off between device sensitivity and uniformity in the GHS array, allowing for enhancement of both objectives. Our results showed that GHS arrays exhibiting >30% variability under single-backgate operation could be compensated using individual tuning to achieve <2% variability with minimal impact on the array sensitivity.

Maglev train high-precision positioning technology based on FBG array time division multiplexing

The positioning system is crucial in operating, controlling and monitoring maglev trains. This study proposes a maglev train positioning technology based on weak Fiber Bragg Grating (wFBG) sensing array time division multiplexing (TDM). The onboard Halbach permanent magnet array generates a traveling wave magnetic field that follows the train movement. The wFBG magnetic sensor array fixed on the track senses the magnetic field changes which cause periodical variation in sensors’ center wavelength. The relative position of the onboard Halbach array and the sensors on the track can be determined with the changes in center wavelength. Therefore, train positioning can be achieved. A rotating motor equipped with a circumferentially arranged magnet array is utilized for simulation experiment to verify the frequency response characteristics of the magnetic sensor. The result is that the operating frequency range of the sensor is at most 160 Hz so the sensor can be applied to positioning under the theoretical speed of 23 km/h. A small-scale experiment containing nine sensors is also conducted, which achieves relative positioning accuracy of 20 mm and absolute positioning accuracy of 540 mm, demonstrating the feasibility of the positioning technology. This study introduces the FBG TDM technology into maglev train positioning and provides new ideas about maglev train positioning.

Solving inverse problems in magnetic field leakage sensor array inspection of petroleum tank floor

The MFL method is a qualitative inspection tool and is a reliable, fast, and economical nondestructive testing method for tank floors. In this paper, before presenting the defect reconstruction procedure, we studied the effect of defect parameters on the magnetic field leakage measured by a single Hall sensor. As predicted, the study of each parameter has demonstrated that any variation in the geometrical parameters of the studied defect induce a significant influence on the MFL signal amplitude and distribution; for this reason, all the defect parameters must be determined precisely and prudently. After that, we have studied the performance of defect shape reconstruction from MFL array sensor imaging and depth estimation while using an iterative inversion method. Indeed, the first stage consists of determining the defect width and length from magnetic flux leakage mapping reconstructed from the recorded signals of the micro-integrated magnetic sensors. As a second step, after coupling Comsol and Matlab software, the defect depth is obtained by coupling the 3D finite elements method and a fast iterative algorithm recently developed. Consequently, the defect shape and size are obtained after a few iterations with a relative error of less than 2%; which makes this method very appropriate for real-time defect reconstruction and quantification. Furthermore, this method of defect reconstruction and seizing can be extended for irregular shape such as cracks and corrosion. In fact, this can be done while subdividing the affected area of non-constant depth into elementary zones of a constant depths. Then, while modifying the previous algorithm, we determine the corresponding depth of each zone.

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources.

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

Signal Processing Using a Circular Sensor Array to Measure the Torsional Angle of a Bolted Joint.

This study presents a new approach to determining the preload force of bolted joints. The concept involves measuring the torsional angle without contact. For this purpose, we present a circular magnetic sensor array integrated into the torque wrench. The torsional angle in bolted joints depends on the dimensions of the screw and the materials used and is typically less than four degrees. For this reason, one requirement is a high angular resolution so that a continuous recording of the torsion angle is feasible during the assembly process. This can be achieved using the circular sensor array and adapted signal processing methods. Two signal processing approaches are utilized. First, the direct method uses the discrete Fourier transformation to calculate the rotation angle from the signal phase. This approach is robust to signal distortion and does not depend on signal amplitude. Second, the method with a learning phase employs Gaussian process regression to minimize the angle error. In an experiment, both approaches were applied within a test bench and showed promising results. The direct method demonstrated a very good angular resolution without training and calibration. For mobile and less-complex applications where a reference system is unavailable, the direct method is preferable. However, in complex measurement systems where reference systems can be utilized initially, significant enhancements to an excellent resolution can be achieved through prior training.

Contactless current measurement for suspended overhead lines using a magnetic field sensor array

AbstractIn this study, a long‐distance non‐contact current measurement method is proposed based on the non‐contact sensor array measurement technology, combined with a new current algorithm. The sensor array is a vertical array composed of three three‐axis magnetic field (MF) sensors that is placed on the ground plane below the measured overhead line. The proposed current algorithm can optimize the geometry of the overhead line using the spatial MF values measured by the sensor array and then the three‐phase currents can be calculated. To improve the accuracy of current measurement, the spatial MF model in the algorithm considers the influence of cable sag. It enables a more accurate coupling matrix between the measured overhead line and the sensor array to be established. Additionally, the current algorithm architecture is designed using a composite algorithm to more optimally obtain the best three‐phase current values. A scale‐down overhead‐line measurement system is developed to test the proposed current measurement method. The results show whether a cable sag exists when the three‐phase currents are approximately balanced, with errors less than 2.0%. Moreover, it can be determined whether cable sag exists when the three‐phase currents are unbalanced, with less than 3.4% errors.

Magnetic photoelectrochemical sensor array utilizing addressing sensing strategy for simultaneous detection of amyloid-β 42 and microtubule-associated protein tau

The accurate diagnosis of diseases can be improved by detecting multiple biomarkers simultaneously. This study presents the development of a magnetic photoelectrochemical (PEC) immunosensor array for the simultaneous detection of amyloid-β 42 (Aβ) and microtubule-associated protein (Tau), which are markers for neurodegenerative disorders. A metal-organic framework (MOF) derivative, Fe2O3@FeS2 magnetic composites with exceptional photoelectric and ferromagnetic properties was synthesized while preserving the original structure and advantages. Thus, the immunoassembly process of the sensor can be carried out in homogeneous solution and recovered by magnetic separation. For simultaneous detection, a chip is divided into multiple independent sensing sites, which have the same preparation and detection environment, allowing for the implementation of a self-calibration method. The sensor array demonstrates considerable detection ranges of 0.01–100 ng mL−1 for Aβ and 0.05–100 ng mL−1 for Tau, with low detection limits of 2.1 pg mL−1 for Aβ and 7.9 pg mL−1 for Tau. The PEC sensor array proposed in this study exhibits exceptional stability, selectivity, and reproducibility, providing a new method for detecting multiple markers.

Magnetic Tactile Sensor with Bionic Hair Array for Sliding Sensing and Object Recognition.

Due to the high application value in intelligent robots, tactile sensors with large sensing area and multi-dimensional sensing ability have attracted the attention of researchers in recent years. Inspired by bionics of hairs on human skin, a flexible tactile sensor based on magnetic cilia array is developed, showing extremely high sensitivity and stability. The upper layers of the sensor are multiple magnetic cilia containing magnetic particles, while the lower layer is a serpentine flexible circuit board with a magnetic sensor array. When magnetic cilia are bent under force, the magnetic sensor array can detect changes in the magnetic field, thereby the magnitude and direction of external force can be obtained. The proposed sensor has a resolution of 0.2 mN with a working range of 0-19.5 mN and can distinguish the direction of external force. The large sensing area and short response time make this sensor suitable for sliding tactile detection, and experiments show that the sensor can be also applied in object recognition with a success accuracy of 97%. In addition to the shape of objects, the sensor can identify whether there is magnetism inside objects, making it of significant value in intelligent robots and modern medicine.

Utilizing the L-curve criterion for the inverse magnetostatic problem of Hall drift current estimation

Aiming at achieving the in-orbit diagnostic of Hall drift current, this study focuses on estimation through the indirect measurement methodology using a magnetic sensor array. It elaborates on the application of a pseudo-seminorm defined for the Hall drift current solution to address the inverse magnetostatic problems, which are formulated with a two-dimensional Tikhonov regularization constraint, and thereby offering a systematic approach to select regularization parameters. Our investigation discusses factors influencing the formation of the L-curve and the accuracy of the resultant solution obtained via the L-curve criterion. The results reveal that the formation of the defined pseudo-seminorm of the Hall drift current solution in the semi-logarithmic coordinate system is independent of the number of calibrating current elements or the number of magnetic sensors. This effectively resolves the issue of failing to generate an L-curve during regularization parameter selection. Furthermore, the study indicates that expanding the number of calibrating current elements—essentially increasing the unknown variables in the inverse magnetostatic equations—contributes to a significant enhancement in the accuracy of Hall drift current solutions. It also has extensibility to be applied to other areas where the contactless current measuring is required.

A Flexible Alternating Current Field Measurement Magnetic Sensor Array for In Situ Inspection of Cracks in Underwater Structure

A Flexible Alternating Current Field Measurement Magnetic Sensor Array for In Situ Inspection of Cracks in Underwater Structure

Predictive model-based correction of magnetic sensor array sway errors

Predictive model-based correction of magnetic sensor array sway errors

Inversion of Target Magnetic Moments Based on Scalar Magnetic Anomaly Signals

As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.

Optimal Design of Magnetic Sensor Arrays for Tunnel Transmission Lines Based on Noncontact Measurement and Differential Evolution Method

Optimal Design of Magnetic Sensor Arrays for Tunnel Transmission Lines Based on Noncontact Measurement and Differential Evolution Method

Underwater Cable Localization Method Based on Beetle Swarm Optimization Algorithm

Dear Editor, This letter concerns the localization of three-core underwater cables which are widely deployed for offshore energy transmission. It is a non-trivial problem, since the external magnetic field of three-core underwater cables is variable which reduces the accuracy of localization. To solve this problem, in this letter, an approximate equation is firstly derived to formulate the external magnetic field of a three-core armored underwater cable by considering the seafloor environments and the structure of three-core cables. Then, a new underwater cable localization method is proposed based on dual three-axis magnetic sensor array and the beetle swarm optimization (BSO) algorithm. The method constructs a fitness function based on the magnetic flux density amplitude and replaces the existing analytical geometry method with an optimization algorithm to achieve underwater cable localization with higher accuracy.

Magnetic Sensor Array Based on Coordinate Measuring and Differential Evolution Algorithm

Magnetic Sensor Array Based on Coordinate Measuring and Differential Evolution Algorithm