Hall Sensor Array Research Articles

Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion

In order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this study proposes a method to identify the damage width of steel wire rope for multi-channel fusion of a Hall sensor array. Firstly, the Hall sensor array is used to capture the magnetic flux leakage data of steel wire rope; then, continuous wavelet transform is used to decompose the original data, and moving average filtering is used to denoise each component; the denoised components are merged and converted into a time spectrum, and the time spectrum is classified by ResNet50 image classification model to realize the detection of wire rope damage width. According to the dataset used in this study, the results show that the proposed method performs best in the mainstream noise reduction model; detection accuracy for the width of damage in steel wire ropes is 97%, which proves that the proposed method is effective and feasible.

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.

Large-area magnetic skin for multi-point and multi-scale tactile sensing with super-resolution

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm2, with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multi-dimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin.

Optimising signal quality for corrosion detection using the innovative Mag_FSRCNN model to minimise hall sensor array in eddy current testing

ABSTRACT The security of aviation hinges significantly on the strength of aircraft structures, with potential risks arising from issues like corrosion impacting the framework. Eddy current testing (ECT) sensors, particularly those utilising array technology, present a promising solution to address this concern. However, challenges persist in improving the clarity of corrosion signals while optimising associated expenses. In response to this, our study introduces the Mag_FSRCNN model, a novel framework that integrates a Fast Super-Resolution Convolutional Neural Network (FSRCNN) module network with a part of Generator module of Generative Adversarial Network to enhance magnetic images obtained by the ECT sensor array. Mag_FSRCNN surpasses traditional methods, showcasing high-resolution capabilities in enhancing ECT signal images. The model demonstrates excellence when evaluated using metrics such as Structural Similarity Index, Root Mean Square Error, and Peak Signal-to-Noise Ratio. These results significantly contribute to the ongoing progress of ECT sensors, facilitating improved resolution and cost-effective application of Hall sensor arrays in the current evolution of ECT sensor technology.

Identification of Rotor Position of Permanent Magnet Spherical Motor Based on Compressed Sensing

The accurate identification of the rotor position of permanent magnet spherical motor (PMSM) is crucial to its closed-loop control. A novel method for rotor position identification of the PMSM based on compressive sensing is proposed, which can obtain accurate rotor position by a limited number of Hall sensors. Firstly, the paper designs a Hall sensor array based on the distribution characteristics of the rotor magnetic and the frequency domain characteristics of the air gap magnetic field, combined with the Fibonacci sequence. Then, an observation matrix construction method is proposed, used for compressed sensing of the radial magnetic field strength information in air gap. Finally, the position of the spherical rotor is identified according to the reconstructed information based on the sensor output value. The identification method is verified by experiments, which shows that the identification accuracy is about 1 degree.

A CMOS Hall Sensors Array With Integrated Readout Circuit Resilient to Local Magnetic Interference From Current-Carrying Traces

This paper proposes a CMOS Hall sensors array with readout circuitry to counteract the magnetic interference from nearby conductors carrying high currents. The 3×3 Hall sensors array with current-spinning collects the spatial magnetic field information in a time-interleaved manner while the readout circuitry, formed by a capacitively-coupled instrumentation amplifier (CCIA) with T-capacitor feedback network, amplifies the transduced signals for subsequent processing. The calibration mechanism analyzes the magnetic field distribution on the array and separates the magnetic field component due to the environment and local perturbation from adjacent traces, enabling accurate detection of the environmental magnetic field. Prototyped in a 0.35-μm CMOS process, the Hall sensors array and readout circuit occupy an area of 2.2 × 2.2 mm2 and consume 3.5 mA of current under a 3.3-V supply. The hall sensors achieve an average sensitivity of 100 V/(A·T). The input-referred noise of the entire circuit is 0.128 μT/√Hz. With the interference cancellation scheme, the error in the magnetic field readings caused by a conductor 5 mm from the center of the chip carrying 3 A of current reduced from 120 to 5 μT, corresponding to a 24× improvement. Overall, the average error of the captured magnetic field after calibration reaches 0.9%.

The MFL detection method by the gradient hall sensors array for the cracks on the top surface of the rails

Cracks on the top surface of the rails due to contact fatigue are usually narrow and variable in direction. Thus, more spatial features need to be explored when using the magnetic flux leakage detection method. In this article, we proposed a way to predict the size and direction of the cracks by the gradient hall sensors array. First, the experimental equipment was designed for leakage field signal detection. Then, the peak time differences of the leakage field signal were analyzed to predict the crack angle, and the magnetic gradient modulus was calculated to predict the crack width. Furthermore, the principal component analysis was performed to reduce the number of eigenvalues. With that, BP neural network algorithm was used to predict the length and depth of the cracks. The experimental results indicate that the method can improve the accuracy of crack prediction, and the average relative errors of the prediction for crack angle, width, length, and depth were 5.7%, 15.3%, 9.2%, and 11.2%, respectively.

Application of toroidal magnet excitation and hall sensor array in magnetic leakage field detection of rail surface defect

To detect rail top surface defects, NdFeB toroidal permanent magnets were firstly used to excite the rail detection area, and the magnetic excitation direction was perpendicular to the rail direction. So that, the magnetic circuit can be analyzed, and the model was verified by finite element simulation. Secondly, the artificial rectangular defect samples were machined on the top surface of the rail, and the magnetic field data acquisition system was developed and equipped on the inspection vehicle. With that, a linear array which consisted of three-dimensional hall sensors was designed to detect the magnetic leakage field signal from the defect samples. Finally, the features of the leakage field signal in each dimension were extracted. And then, the length, width, and depth of the defects were predicted using BP neural network algorithm. The results indicate that the method can improve the excitation efficiency, and the average absolute errors of prediction for defect length, width, and depth were 1.43 mm, 1.01 mm, and 0.63 mm respectively.

A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zones (stress defects) cause stress corrosion of ferromagnetic materials, and also have the potential to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement methods is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.

A Moving Magnetic Grid-Type Long-Range Linear Absolute Displacement Sensor

In order to overcome the shortcomings of the traditional magnetic absolute linear displacement sensors in which cables affect the flexibility and measurement range in linear motor transmission systems, this paper proposes a novel cable-free moving magnetic grid-type long-range absolute displacement sensor. The sensor consists of a magnetic grid and a signal acquisition board. The magnetic grid is a moving component that contains two rows of permanent magnet arrays, one for relative displacement measurement and the other for the displacement interval code. The signal acquisition board is a fixed component that uses n groups of two-row Hall sensor arrays for continuous absolute displacement measurement. The principle of the sensor using the 2D magnetic field signal for the relative displacement measurement is analyzed, and a measurement method based on Hall sensor arrays for coding and absolute displacement detection over n cycles is proposed. Finally, a sensor prototype is fabricated and the experiments are performed. The experimental results show that the measurement resolution of the sensor is 5 μm, and the measurement accuracy is ±14.8 μm within the measurement range of 0-98.3 mm. The proposed sensor can realize continuous absolute displacement measurement over multiple cycles without cable binding.

Comparison of Magneto-Optical Faraday Effect Method and Magnetic Camera for Imaging the 2-D Static Leakage Magnetic Flux Density

Magnetic flux leaks around the defect on the magnetized ferromagnetic specimen. The magnetic flux leakage testing (MFLT) use the magnetic sensor to measure the leaked magnetic flux density (LMFD). The high sensitivity of sensor need to increase the probability of detection (POD). Also the small size and high spatial resolution of sensor need to increase POD and to sizing defect. Uniform and small lift-off are another necessary to increase POD and sizing ability. Also, the real time imaging of magnetic flux density can increase the inspection time. So the period of planned preventive maintenance could reduce. Two dimensional semiconductor-based magnetic sensor array with high spatial resolution satisfies these requirements. Projection of magnetic flux density on a sensor itself would be another method. We compare the magnetic camera and the magneto-optical Faraday effect (MOE) for MFLT. Magnetic camera uses the two dimensional Hall sensor arrays to imaging the LMFD. And the MOE project LMFD. The plane of polarized light rotates inside of magneto-optical film (MOF). The Faraday rotation angle proportional to direction and intensity of magnetic flux density. Sensitivity, spatial resolution, magnetic hysteresis, repeatability, full-scaled operational range compared. Also shape of target specimen compares too.

6-D Spatial Localization of Wireless Magnetically Actuated Capsule Endoscopes Based on the Fusion of Hall Sensor Array and IMU

This paper introduces a novel 6 degrees of freedom (6-D) spatial localization for wireless magnetically actuated capsule endoscopes (WMACEs) based on multi-sensor fusion. A WMACE integrated with an inertial measurement unit (IMU) sensor is developed, which enables wireless transmission of video and sensor data. A Hall sensors array is designed to measure the magnetic field generated by the WMACE for acquiring its 6-D pose under external magnetic actuation. The angle between the direction of the WMACE’s magnetic moment and the gravity direction measured by the IMU is set initially as 90°, which, based on multi-sensor data fusion, solves the problem that the rotation around the magnetization axis cannot be acquired by Hall sensors array and that the rotation around the gravity axis cannot be measured by the IMU. In addition, eight pose detection algorithms and two data-fusion filters are experimentally compared for analyzing and selecting the optimal approach for the 6-D localization of the WMACE. The experimental results indicate a high accuracy for 6-D pose detection, with a mean absolute error of 1.463 mm for 3-D position and 0.419° for 3-D orientation, with the height between the capsule and the Hall sensor array ranging from 25 mm to 72 mm.

Identification of Euler angles of permanent magnet spherical motor rotor based on Hall sensors array

Rotor position detection is an indispensable link in the closed-loop control system of permanent magnet spherical motors. This paper proposes a method to determine the position of the rotor by calculating the Euler angles using the Hall sensors array. The accuracy of Euler angle calculation is related to the installation position and quantity of the sensors. This article uses a spherical quadrilateral triangle model to determine the location of the Hall sensors. According to the different rotation axis around which the rotor rotates, the projection planes are calculated respectively. The output result of the Hall sensor array is converted into an encoded signal. The rotor magnetic pole coordinates are calculated according to the encoded signal. The coordinates are projected onto the corresponding projection plane, and the angle of rotation of the rotor is calculated. When the angles obtained at multiple consecutive sampling moments are the same, the angle corresponding to the intermediate moment is used as a sign quantity, and the angle corresponding to each sampling time is calculated according to the sign quantity. Finally, in order to verify the correctness of the method proposed in this paper, the experiments are carried out to verify the different motion postures of the spherical motor, and the experimental results verify the feasibility of the proposed method for calculating Euler angles of the permanent magnet spherical motor.

Magnetic Float Liquid Level Detection Method for High-Temperature Superconducting Flux-Pinning Maglev System

High temperature superconducting (HTS) flux-pinning maglev system uses the flux-pinning characteristics of HTS bulk to achieve self-stable levitation, but the superconductivity of HTS bulk depends on low temperature maintained by liquid nitrogen. It can be seen that the real-time detection of liquid nitrogen level is an essential safety detection in the HTS flux-pinning maglev system. However, the liquid nitrogen level measurement of Dewar is limited by many conditions, such as small available size of Dewar, small liquid nitrogen capacity, low temperature characteristics of liquid nitrogen, etc. These conditions require the liquid level detection system to be able to withstand low temperature, have sufficient identification accuracy and have no significant impact on the cold insulation performance of Dewar. All of these bring difficulties to the design of real-time liquid level detection in the maglev system. Based on the structural characteristics of Dewar, a new type of magnetic float liquid level detection device is studied in this paper. The device can realize liquid nitrogen level detection function and low-level alarm function. Its technical principle is to use Hall sensor array to detect the position of the magnetic float in real time, and transmit the data to the onboard computer for data storage and data monitoring. The experimental results show that the proposed liquid nitrogen level detection method can meet the requirements of liquid nitrogen level detection and alarm with certain accuracy, and will not bring additional heat leakage to affect the Dewar performance.

Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers

This article presents a contact fault diagnosis method of gas-insulated switchgear (GIS) plug-in connector via magnetic field measurement, magnetic field visualization, and deep neural network (DNN) classifiers. First, the surrounding magnetic field of GIS plug-in connector with normal contact (NC) condition and with artificially designed contact failures was measured by the Hall sensor array. Then, the measured magnetic field was gathered with an original matrix of 16 <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 16 dimensions. The <inline-formula> <tex-math notation="LaTeX">$\vert$</tex-math> </inline-formula>original matrix was then visualized by the max&#x2013;min normalization and correlation matrix. Database containing 11 000 magnetic field images was labeled and segmented as training, validation, and test datasets. Furthermore, high-dimensional features of input magnetic field images were extracted by different DNN filters, including convolutional neural network (CNN), simple recurrent neural network (Sim-RNN), and long short-term memory (LSTM) network. Then, extracted high-dimensional features were fed into a fully connected (Fc) neural network with SoftMax classifiers to identify different contact faults. Finally, the performance of different DNN-based classifiers is compared by the fault classification merits, <inline-formula> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula>-distributed stochastic neighbor embedding (<inline-formula> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula>-SNE) feature clustering, and confusion matrixes. Results show that the DNN-based model could achieve contact fault classification task with an accuracy of 97.7% and <inline-formula> <tex-math notation="LaTeX">$F_{1\_\mathrm{score}}$</tex-math> </inline-formula> of 0.985. Therefore, the proposed method is useful for designing a high-performance contact status monitoring system of GIS equipment, thus improving its operation safety.

Design and Application of MEMS-Based Hall Sensor Array for Magnetic Field Mapping

A magnetic field measurement system based on an array of Hall sensors is proposed. The sensors are fabricated using conventional microelectromechanical systems (MEMS) techniques and consist of a P-type silicon substrate, a silicon dioxide isolation layer, a phosphide-doped cross-shaped detection zone, and gold signal leads. When placed within a magnetic field, the interaction between the local magnetic field produced by the working current and the external magnetic field generates a measurable Hall voltage from which the strength of the external magnetic field is then derived. Four Hall sensors are fabricated incorporating cross-shaped detection zones with an identical aspect ratio (2.625) but different sizes (S, M, L, and XL). For a given working current, the sensitivities and response times of the four devices are found to be almost the same. However, the offset voltage increases with the increasing size of the detection zone. A 3 × 3 array of sensors is assembled into a 3D-printed frame and used to determine the magnetic field distributions of a single magnet and a group of three magnets, respectively. The results show that the constructed 2D magnetic field contour maps accurately reproduce both the locations of the individual magnets and the distributions of the magnetic fields around them.

A CMOS Hall sensor modeling with readout circuitry and microcontroller processing for magnetic detection.

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 μm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood.

Robotic Localization Based on Planar Cable Robot and Hall Sensor Array Applied to Magnetic Capsule Endoscope.

Recently an active locomotive capsule endoscope (CE) for diagnosis and treatment in the digestive system has been widely studied. However, real-time localization to achieve precise feedback control and record suspicious positioning in the intestine is still challenging owing to the limitation of capsule size, relatively large diagnostic volume, and compatibility of other devices in clinical site. To address this issue, we present a novel robotic localization sensing methodology based on the kinematics of a planar cable driven parallel robot (CDPR) and measurements of the quasistatic magnetic field of a Hall effect sensor (HES) array. The arrangement of HES and the Levenberg-Marquardt (LM) algorithm are applied to estimate the position of the permanent magnet (PM) in the CE, and the planar CDPR is incorporated to follow the PM in the CE. By tracking control of the planar CDPR, the position of PM in any arbitrary position can be obtained through robot forward kinematics with respect to the global coordinates at the bedside. The experimental results show that the root mean square error (RMSE) for the estimated position value of PM was less than 1.13 mm in the X, Y, and Z directions and less than 1.14° in the and orientation, where the sensing space could be extended to ±70 mm for the given 34 × 34 mm2 HES array and the average moving distance in the Z-direction is 40 ± 2.42 mm. The proposed method of the robotic sensing with HES and CDPR may advance the sensing space expansion technology by utilizing the provided single sensor module of limited sensible volume.

Coil Positioning for Wireless Power Transfer System of Automatic Guided Vehicle Based on Magnetic Sensing.

As an auxiliary function of the wireless power transfer (WPT) system, coil positioning can solve the power and efficiency degradation during power transmission caused by misalignment of the magnetic coupler. In this paper, a Hall sensor array is used to measure the change of magnetic flux density. By comparing the multisensor data fusion results with the preset data obtained from the coil alignment, the real-time accurate positioning of the receiving coil can be realized. Firstly, the positioning model of the receiving coil is built and the variation of magnetic flux density with the coil misalignment is analyzed. Secondly, the arrangement of the Planar 8-direction symmetric sensor array and the positioning algorithm based on data fusion of magnetic flux density variations are proposed. In order to avoid coil positioning misalignment caused by the unstable magnetic field distribution which is actually affected by the change of mutual inductance during automatic guided vehicle (AGV) alignment, the constant current strategy of primary and secondary sides is proposed. Finally, the coil positioning experimental platform is built. The experimental results show that the coil positioning method proposed in this paper has high accuracy, and the positioning error is within 4 cm.

A Novel Method for Locating a Magnetic-Assisted Capsule Endoscope System

In this article, we propose a method for locating a magnetic capsule endoscope, which is utilized to observe the gastrointestinal tract, especially for colon. Recent studies mainly focus on either single-magnet positioning or multiple magnets positioning with similar scale in magnetic parameters, whereas these methods cannot be directly applied to the proposed magnetic-assisted capsule endoscope system, in which two magnets with big differences in magnetic parameters are used. An algorithm based on the Levenberg–Marquardt algorithm and a Hall sensor array is presented to solve this problem. Three different experiments have been carried out to validate the feasibility and effectiveness of the proposed method for the magnetic capsule positioning and orientation.