Detection Of Metallic Objects Research Articles

A Low Power IPT System With Integrated Magnetics and Hybrid Tuning for Metal Object Detection

A Low Power IPT System With Integrated Magnetics and Hybrid Tuning for Metal Object Detection

Evaluation Modeling and Improvement Method for Sensitivity of Metal Object Detection in EV Wireless Charging System

Evaluation Modeling and Improvement Method for Sensitivity of Metal Object Detection in EV Wireless Charging System

Metal object detection with high sensitivity and blind-zone free for DD coil-based wireless electric vehicle chargers

Metal object detection with high sensitivity and blind-zone free for DD coil-based wireless electric vehicle chargers

A metal object detection system for electric vehicle wireless charging based on LC oscillating circuit and reverse-winding-incorporated detection coils

In magnetically coupled wireless charging systems for electric vehicles, it is common for metal objects to enter the charging system. In this case, the strong magnetic field of wireless charging causes an eddy current effect in the metal object, which generates a large amount of heat. This heat accumulation poses a risk of fire. In this paper, a novel metal object detection system designed for wireless charging of electric vehicles is proposed. The system utilizes the principle that the coil impedance and coupling coefficient change with the presence of metal objects, and a set of composite coils is designed to improve the sensitivity of detection. The detection coil and feedback coil in the composite coil are decoupled by reverse series connection. An LC oscillating circuit based on transformer feedback is used to detect metal objects, and the circuit can make good use of the parameter changes of the composite coil. Finally, an experimental platform for wireless charging metal detection in electric vehicles has been built to validate the proposed system. Through testing, it has been demonstrated that the system can detect the presence of metal objects in a timely and accurate manner.

Numerical and Experimental Investigation of Time-Domain-Reflectometry-Based Sensors for Foreign Object Detection in Wireless Power Transfer Systems

Foreign object detection (FOD) is considered a key method for detecting objects in the air gap of a wireless charging system that could pose a risk due to strong inductive heating. This paper describes a novel method for the detection of metallic objects utilizing the principle of electric time domain reflectometry. Through an analytical, numerical and experimental investigation, two key parameters for the design of transmission lines are identified and investigated with respect to the specific constraints of inductive power transfer. For this purpose, a transient electromagnetic simulation model is established to obtain and compare the sensor impedance and reflection coefficients with experimental data. The measurement setup is based on parametrically designed sensors in laboratory scale, using an EUR 2 coin as an exemplary test object. Consequently, the proposed simulation model has been successfully validated in this study, providing a comprehensive quantitative and qualitative analysis of the major transmission line design parameters for such applications.

Metal Object Detection With Detection Coils Perpendicular to Power Coils for Wireless Power Transfer Systems

In this paper, a metal object detection (MOD) method with detection coils perpendicular to power coils is proposed. Without a metal object, the induced voltage in the detection coils is ideally zero. When a metal object falls on the top of the Tx pad, An eddy current is induced and this eddy current in the metal object will induce a voltage in the detection coils. The proposed method can perform well with misalignments between the Tx and Rx pads. No adjustments are required for different winding positions. The concept is well verified with experimental results.

Improved Metal Object Detection Circuits for Wireless Charging System of Electric Vehicles

Improved Metal Object Detection Circuits for Wireless Charging System of Electric Vehicles

(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects.

Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by using machine learning and computer vision techniques. This paper describes a novel grasping strategy based on a groundbreaking geometrical analysis which extracts diametrically opposite points taking into account surface smoothing (even those target objects that might conform highly complex shapes) to guarantee the uniformity of the grasping. It uses a monocular camera, as we are often facing space restrictions that generate the need to use laparoscopic cameras integrated in the tools, to recognize and isolate targets from the background, estimating their spatial coordinates and providing the best possible stable grasping points for both feature and featureless objects. It copes with reflections and shadows produced by light sources (which require extra effort to extract their geometrical properties) in unstructured facilities such as nuclear power plants or particle accelerators on scientific equipment. Based on the experimental results, utilizing a specialized dataset improved the detection of metallic objects in low-contrast environments, resulting in the successful application of the algorithm with error rates in the scale of millimeters in the majority of repeatability and accuracy tests.

Bipolar Checkerboard Metal Object Detection Without Blind Zone Caused by Excitation Magnetic Field for Stationary EV Wireless Charging System

Metal object detection (MOD) technology provides a guarantee for the safe operation of electric vehicle (EV) wireless charging system. To solve the problem of detection blind zone caused by excitation magnetic field in the existing MOD technology, a bipolar checkerboard MOD technology is proposed. By adding an excitation source, not only the blind zone is eliminated, but also it makes the EV wireless charging system and the MOD system work independently. Firstly, the positions and causes of the blind spot caused by the excitation magnetic field of the Tx coil in the EV wireless charging system are analyzed. Secondly, the structure and principle of the bipolar checkerboard MOD system are analyzed. Thirdly, a definition of the detection sensitivity of MOD is given, and the relationship between sensitivity and the size parameters of bipolar checkerboard sensing coil sets is analyzed.Moreover, the parameter optimization method of the sensing coil sets and detection circuit is analyzed. Finally, a MOD system experimental setup is built to verify its feasibility. The results show that the highest sensitivity can reach 20% for a square aluminum plate of 80×80×1 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> without a blind zone and the two systems can work independently with each other.

Robotic Detector with Rocker Bogie Mechanism

The Rocker Bogie Mechanism (RBM) is widely used in planetary rovers for its ability to traverse challenging terrain. However, the presence of metal debris in the terrain can pose a significant threat to the rover's mobility and instrumentation. Therefore, a metal detector integrated with the RBM is necessary to avoid any potential damage. This paper presents the design and development of a metal detector for the RBM, which includes the selection of suitable components, the development of the detection algorithm, and the integration with the RBM. The detector's performance is evaluated in both simulation and experimental settings, demonstrating the successful detection of metallic objects and their location.

A metal object detection system for electric vehicle wireless charging with quadrupole detection coils

In the practical application of magnetic coupling wireless charging technology, the absence of direct contact between the receiver and transmitter usually leads to metal objects (MOs) entering the charging area. The strong magnetic field between the receiver and transmitter causes eddy current effects and generates large amounts of heat in MOs, which may lead to serious hazards such as fires, so it is necessary to detect accidental entry of MOs. In this paper, a novel detection coil layout suitable for wireless charging of electric vehicles (EVs) has been proposed, which consists of multiple quadrupole coils arranged to achieve decoupling from the receiver and the transmitter to minimize the effect on the system efficiency. The influence of the presence of MOs on the equivalent inductance and equivalent resistance of the quadrupole detection coil has been deduced. The simulation results show that the maximum self-inductance change rate of the proposed detection coil reaches 28.11%, and a high self-inductance variation rate is also achieved at the edge of the detection coil.

Rapid Object Depth Estimation From Position-Referenced EMI Data Using Machine Learning

State-of-the-art methods for localization and detection of small metallic objects using electromagnetic induction (EMI) sensing usually struggle due to the strong correlation between the intrinsic parameters of the object and the object’s depth. In this article, we present a machine-learning-based approach for rapid estimation of metallic object depth from line-scan EMI data. The 1-D-convolutional neural network (1D-CNN), trained on a simulated dataset, takes advantage of metal detector (MD) spatial response to extract features from which depth is inferred. Experimental evaluation using a mono-coil pulse induction MD and an electromagnetic (EM) tracking system was performed under laboratory conditions on a large dataset containing arbitrarily oriented objects of different sizes, shapes, and materials. The nonlinear least-squares (NLSs) inversion was employed as the benchmark method for comparison. The proposed solution shows superior performance over NLS at depths >10 cm. From two passes of MD over the object at depths within the range of 2.5–15 cm, our method yields a median absolute error (MedAE) on the order of millimeters.

Blind-Zone-Free Metal Object Detection for Wireless EV Chargers Employing DD Coils by Passive Electromagnetic Sensing

Metal object detection (MOD) is crucial for the safe operation of ground-assembled wireless electric vehicle charging (WEVC) systems. Electromagnetic sensing by passive coils is a reliable MOD method for high-power applications, such as WEVC. Previous designs of sensing coils are mainly targeted at the systems with unpolarized coils (e.g., circular coils). However, they are not suitable for those with DD-coil-coupling engaged due to the fundamentally different field pattern. To fix this problem, in this article, a field-oriented design of sensing coils is proposed, which mainly aims at blind-zone elimination while maintaining high sensitivity and cost-effectiveness. The blind zone arising from axial symmetry of the field is removed by size-modulated C-shape coil units. And removing the central blind spot relies on an extra patch coil by utilizing the characteristic of parallel magnetic flux generated by DD coils. Tested by 13 positions (central blind spot excluded) where MOs might exist using a 3-kW WEVC prototype, the optimized sensing coils completely remove the axial blind zones when 50 mV is set as the threshold voltage. Moreover, for the central blind spot, the proposed patch coil can stably identify MO intrusion no matter if the prototype operates in light-load or heavy-load conditions.

A Shared Method of Metal Object Detection and Living Object Detection Based on the Quality Factor of Detection Coils for Electric Vehicle Wireless Charging

High power alternating magnetic fields can cause lesions and pathological changes in living object bodies entering the wireless charging area. Also, as for metal objects, the eddy effect can cause the security risks such as fire and ignition hazards. Hence, metal object detection (MOD) and living object detection (LOD) are the necessary functions for a wireless power transfer (WPT) system. A shared method of MOD and LOD based on the quality factor of detection coils for electric vehicle wireless charging was proposed in this paper to realize the dual functions by the same detection coil array. Firstly, the effect of metal objects and living objects on the detection coil quality factor was analyzed in detail. Secondly, a method of real-time calculation of quality factor was proposed to detect whether the foreign object exists. Then, a distinction algorithm based on the phase variation trend of resonant capacitor voltage was designed to classify the type of foreign objects. Finally, a 3.3 kW experimental prototype, including a foreign object detection (FOD) system, was established to verify the validity. The advantage of the quality factor as the core indicator for FOD was analyzed in detail. Under the condition of an iron wafer, aluminum wafer, and beef chunk placed on the detection coil, the quality factor calculation results of the detection coil in contrast to those without foreign objects decreased by 80.8%, 50.5%, and 69.9%, respectively. Meanwhile, the phase difference of the excitation relative to the resonant capacitor voltage changed -61.3°, -74.6°, and 49.4°, respectively.

Preface

The 2022 International Conference on Power Engineering and Electrical Technology (ICPEET 2022) was successfully held in Nanjing, China from July 22nd to 24th, 2022 in the form of online conference. The conference aimed to provide a platform for experts and scholars in the fields of power engineering and electrical technology to share research achievements and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen discussion, and promote industrial cooperation of academic achievements.We had the honor of having invited Prof. Hongwei Li from Southwest Petroleum University, China to serve as our Conference General Chair. About 160 individuals and enterprises all over the world attended the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one.During the conference, we were delighted and honored to invite three sophisticated keynote speakers to address their speeches. Among them, Assoc. Prof. Kwan Yiew Lau from Universiti Teknologi Malaysia delivered a speech on the topic: Tailoring Dielectric Properties of Polymer Nanocomposites. To address the problems hindering the use of nanocomposites as practical high voltage insulation materials, he proposed nanofiller/polymer interface modification techniques to tailor the dielectric properties of nanocomposites. The importance of the local material structure in determining the dielectric properties of nanocomposites was also discussed. Additionally, Professor Yiming Zhang from Fuzhou University gave a report on Foreign Object Detection Technology in Electric Vehicle Wireless Charing System. In the report, different foreign object detection techniques were reviewed and a new metal object detection system with novel coil layouts and topologies was proposed. The wonderful and dramatic speeches of each keynote speaker had triggered heated discussion. Moreover, every participant praised this conference for disseminating useful and insightful knowledge.The proceedings of ICPEET 2022 is a compilation of the accepted papers and represent an interesting outcome of the conference. These papers feature but are not limited to the following topics: Electric Power Allocation, Smart Grid and Technologies, Power System, Electric Vehicle Technology, Electromagnetic Compatibility, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.On behalf of the conference organizing committee, we would like to express our heartfelt appreciation to all the keynote speakers, peer reviewers, and all the participants. In particular, we would like to acknowledge the Journal of Physics: Conference Series, for the endeavor of all the colleagues in publishing this paper volume. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.List of Committee member is available in this pdf.

Neural Network for Both Metal Object Detection and Coil Misalignment Prediction in Wireless Power Transfer

This study proposes a method for wireless power transfer (WPT) systems to identify the existence of foreign metal objects and simultaneously predict the misalignment distance between the primary and secondary coils. The proposed method is based on a neural network (NN) trained using electromagnetic field simulations. The training data for the NN consist of differential voltages in the detection coils, together with the input voltage of the primary coil. Although the metallic objects and coil misalignment induce confusing voltages, the trained NN exhibits over 90% accuracy for the validation dataset and mean prediction errors of less than 1 mm for the misalignment distance and ground clearance variance.

A super-sensitive metal object detection method for DD-coil-engaged wireless EV chargers by passive electromagnetic sensing

Metal object detection (MOD) is very important to guarantee the thermal safety of wireless charging systems for electric vehicles (EVs). Regarding the MOD methods based on passive sensing coils, most previous designs are targeted at the systems employing unpolarized coils, and the detection criteria are voltage-difference-based (VDB). Unfortunately, they are unsuitable for those employing DD coils due to the fundamental difference of field pattern. In addition, the VDB method has an inherent problem of low sensitivity. To fix these two problems, in this work, a set of field-oriented sensing coils is devised, which successfully realizes blind-zone elimination and super-high sensitivity. The arrangement of sensing coils accords with the pole-to-pole field distribution of DD coils to highlight the influence of MOs. The blind zone caused by the axial symmetry of the coupling field is removed by a non-symmetric patch coil. Moreover, a MOD mechanism with new criteria for MO identification is developed, which is the core of super-high sensitivity. Tested by ten positions where MOs might intrude using a 3 kW prototype, the optimized sensing coils can entirely remove the y-axis blind zones. The average sensitivity for the charging area reaches up to 16.

Metal Object and Vehicle Position Detections Integrated With Near-Field Communication for Wireless EV Charging

To commercialize wirelesselectric vehicle charging (WEVC), three critical issues must be resolved: communication between the vehicle assembly (VA) and the ground assembly (GA), metal object detection (MOD) on the GA coil, and position detection (PD) of the VA. This paper proposes a comprehensive system that integrates MOD and PD with VA-GA near-field communication (NFC). A common high-frequency aided carrier is used not only for NFC, but also for MOD and PD. In addition, an array of dual-purpose detection coils is proposed as a sensor for MOD and PD. As a result, the three functions listed above share numerous components and circuits, greatly simplifying the system structure. Furthermore, because the high-frequency aided carrier can operate with or without a power carrier, the preceding three functions are independent of system power status. The proposed method has been validated using a 3.3 kW WEVC prototype operating at 85 kHz. The experimental results show that the proposed system is capable of performing both online and offline NFC, MOD, and PD with high sensitivity while having no impact on wireless charging efficiency.

Metal Object Detection by Monitoring Fifth-Order Harmonic Current of IPT System With Dual Frequency Tuning

A key challenge for safe operation of inductive power transfer (IPT) systems is to detect the presence of metal objects (MOs), particularly when a power receiver (Rx) is misaligned from a power transmitter (Tx) during online charging. This letter proposes a metal object detection (MOD) method by monitoring the fifth-order harmonic current of a dual-frequency IPT system driven by a voltage-fed inverter with square-wave output. While power transfer is maintained by tuning at the fundamental frequency, the Tx circuit is tuned simultaneously at the fifth-order frequency to ease the detection of the fifth-order harmonic current for MOD. The Rx circuit is also fully tuned at the fifth-order frequency, but the fifth order current is completely blocked. With the proposed dual-frequency tuning configuration, the presence of MO causes a significant drop in the fifth-order harmonic current at the Tx side, whereas the Rx behaves as an open circuit with no effect on power transfer. Circuit and magnetic field simulation results show the fifth-order harmonic current presents a unique response to the existence of MO, which is in good agreement with practical measurements. Experimental results demonstrate that at a constant 10&#x00A0;W power output, both nonferrous and ferrous MOs within the Tx charging area can be 100&#x0025; detected, even when the Rx is misaligned by 50&#x0025; of the Tx coil radius.

Detection of metallic objects on digital radiographs with convolutional neural networks: A MRI screening tool

IntroductionScreening for metallic implants and foreign bodies before magnetic resonance imaging (MRI) examinations, are crucial for patient safety. History of health are supplied by the patient, a family member, screening of electronic health records or the picture and archive systems (PACS). PACS securely store and transmits digital radiographs (DR) and related reports with patient information. Convolutional neural networks (CNN) can be used to detect metallic objects in DRs stored in PACS. This study evaluates the accuracy of CNNs in the detection of metallic objects on DRs as an MRI screening tool. MethodsThe musculoskeletal radiographs (MURA) dataset consisting of 14.863 upper extremity studies were stratified into datasets with and without metal. For each anatomical region: Elbow, finger, hand, humerus, forearm, shoulder and wrist we trained and validated CNN algorithms to classify radiographs with and without metal. Algorithm performance was evaluated with area under the receiver-operating curve (AUC), sensitivity, specificity, predictive values and accuracies compared with a reference standard of manually labelling. ResultsSensitivities, specificities and area under the ROC-curves (AUC) for the six anatomic regions ranged from 85.33% (95% CI: 78.64%–90.57%) to 100.00% (95% CI: 98.16%–100.00%), 75.44% (95% CI: 62.24%–85.87%) to 93.57% (95% CI: 88.78%–96.75%) and 0.95 to 0.99, respectively. ConclusionCNN algorithms classify DRs with metallic objects for six different anatomic regions with near-perfect accuracy. The rapid and iterative capability of the algorithms allows for scalable expansion and as a substitute MRI screening tool for metallic objects. Implications for practiceAll CNNs would be able to assist in metal detection of digital radiographs prior to MRI, an substantially decrease screening time.