International Journal of Applied Electromagnetics and Mechanics - Volume Pre-press, issue Pre-press

Authors: Ma, Yangyang | Li, Yongjian | Chen, Ruiying | Yue, Shuaichao | Sun, He

Article Type: Research Article

Abstract: With the increase in power electronic equipment in power system, the excitation of ferromagnetic materials often involves a large number of harmonics. Therefore, it is necessary to construct an accurate dynamic hysteresis model to adapt to this complicated operating state of electrical equipment. In this paper, a Hybrid Dynamic Hysteresis Model (HDHM), which can effectively characterize the harmonic excitation of materials is studied based on the Preisach model and Stacked Auto-Encoder (SAE) model. The static part of this model takes the form of the inverse Preisach model. And the Multiple Dynamic Hysteresis Model Set (MDHMS) is constructed by multiple dynamic …models of eddy currents and excess characteristics of the ferromagnetic materials. The dynamic part of the HDHM takes the form of the model structure combining the Stacked Auto-encoder and the MDHMS. The calculation results of the hysteresis loop and ferromagnetic loss in the harmonic condition of silicon steel sheet proves the validity of this model. Moreover, compared with the conventional dynamic hysteresis model, the HDHM has better accuracy and generalization ability. Show more

Keywords: Hybrid dynamic hysteresis model, the Preisach model, multiple-model set theory, ferromagnetic materials, iron loss

DOI: 10.3233/JAE-220112

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Rao, Shaowei | Yang, Shiyou | Tucci, Mauro | Barmada, Sami

Article Type: Research Article

Abstract: In this contribution a methodology to diagnose transformer faults based on Dissolved Gas Analysis (DGA) by using a convolutional neural network (CNN) is proposed. The algorithm to transform the gas contents (resulting from the DGA analysis) into feature maps is introduced, and the resulting feature maps are the input of the CNN. In order to take into account the fact that the data set is imbalanced, the improved Synthetic Minority Over-Sampling Technique (SMOTE) is combined with the data cleaning technique to protect the CNN from training bias. The effect of the CNN architecture on the classification performance is also investigated …to determine the optimal CNN parameters. All the above mentioned possibilities are tested and their performance investigated; in addition, a final test on the IEC TC 10 transformer fault database validates the accuracy and the generalization potential of the proposed methodology. Show more

Keywords: Convolutional neural network, deep learning, DGA, fault diagnosis, SMOTE, transformer

DOI: 10.3233/JAE-230011

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Kłosowski, Grzegorz | Rymarczyk, Tomasz | Wójcik, Dariusz

Article Type: Research Article

Abstract: The main problem with any tomography is the transformation of measurements into images. It is the so-called “inverse problem,” which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent …layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality. Show more

Keywords: Electrical tomography, industrial tomography, inverse problem, LSTM networks, autoencoders

DOI: 10.3233/JAE-230013

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Jiang, Hao | Zhang, Hongwei | Chen, Jing | Xiao, Sa | Miao, Xiren | Lin, Weiqing

Article Type: Research Article

Abstract: The top oil temperature in ultra-high voltage (UHV) reactors has attracted enormous interest due to its wide applications in fault diagnosis and insulation evaluation. In this work, the precise prediction method based on the Seq2Seq module with the convolutional block attention mechanism is proposed for the UHV reactor. To reduce the influence of vibratility and improve computational efficiency, a combination of the encoding layer and decoding layer named Seq2Seq is performed to reconstruct the complex raw data. The convolutional block attention mechanism (CBAM), composed of spatial attention and channel attention, is utilized to maximize the use of information in data. …The Seq2Seq-CBAM is established to forecast the variation tendency of the oil temperatures in the UHV reactor. The experimental results show that the proposed method achieves high prediction accuracy for the top oil temperature in both single-step and multi-step. Show more

Keywords: UHV reactor, top oil temperature, attention, convolution block attention mechanism (CBAM), online detection scenario

DOI: 10.3233/JAE-230022

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Mikami, Ryosuke | Sato, Hayaho | Hayashi, Shogo | Igarashi, Hajime

Article Type: Research Article

Abstract: This paper proposes a multi-objective optimization method for permanent magnet motors using a fast optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and deep learning. Multi-objective optimization with topology optimization is effective in the design of permanent magnet motors. Although CMA-ES needs fewer population size than genetic algorithm for single objective problems, this is not evident for multi-objective problems. For this reason, the proposed method generates training data by solving the single-objective optimization multiple times using CMA-ES, and constructs a deep neural network (NN) based on the data to predict performance from motor images at high speed. The deep NN …is then used for fast solution of multi-objective optimization problems. Numerical examples demonstrate the effectiveness of the proposed method. Show more

Keywords: Deep learning, CNN, multi-objective optimization, CMA-ES, NSGA-II, PM motor

DOI: 10.3233/JAE-230077

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Rymarczyk, Tomasz | Kulisz, Monika | Kłosowski, Grzegorz

Article Type: Research Article

Abstract: This study concerns research on using electrical impedance tomography (EIT) to image moisture inside the porous walls of buildings. In order to transform the electrical measurements into the values of the reconstructed 3D images, a neural network containing the LSTM layer was used. The objective of the study was to evaluate the impact of various loss functions on the efficacy of a neural network’s learning process. During the training process, three distinct variations of the loss function were employed, namely mean squared error (MSE), Huber, and a hybrid of MSE + Huber, to attain the desired outcome. Given that the …primary focus of the study was on the loss function, the particular neural network architecture employed was deemed non-essential. In order to minimize the influence of the neural network architecture on the outcomes of the test, a comparatively uncomplicated neural model was implemented, comprising a solitary LSTM layer and a single fully connected layer. Show more

Keywords: Machine learning, neural networks, electrical tomography, moisture inspection

DOI: 10.3233/JAE-230083

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Hizem, Moez | Ben Saada, Aymen | Ben Mbarek, Sofiane | Choubani, Fethi

Article Type: Research Article

Abstract: Human-Like digital models have been around for quite some time. They significantly contributed to the increase of the accuracy of the whole-body-average specific absorption rate estimations. However, the anatomical and morphological diversity between human beings has not yet been embraced by the actual anthropomorphic models for several reasons such as financial costs, excessive exposure of volunteers to electromagnetic waves, and the required number of technical experts needed to build one voxelized model. Recently, machine learning has been used to reduce the complexity of certain tasks. Yet, at least, having an anthropomorphic model per nation is still far away to achieve. …To reduce the building cost of new human-like models, we build on the success of anthropomorphic models and machine learning to derive mathematical equations that make it possible to predict the Whole-body-average SAR from low frequencies up to twice the resonance frequency without any cost and excessive electromagnetic exposure of new volunteers. The completely new machine learning based equations are applicable for any age, ethnic group, and for both genders. They depend only on the human body’s morphological (height and weight) and anatomical parameters (tissue weights). In this work, we first address the whole-body-average SAR peak and we present a set of two estimators. In second, we show that the resonance frequency is not only a function of the height of the human body, to end up with a third estimation for the resonance frequency. These completely new estimators are finally combined into a novel function that links the whole-body-average SAR to the frequency. It shows the accurate prediction for low frequencies (10 MHz) up to twice the resonance frequency. The derived estimators for the maximum WBASAR and the resonance frequencies showed better results for low frequency exposure. Show more

Keywords: SAR, exposure, frequency, voxel, anthropomorphic, dosimetry

DOI: 10.3233/JAE-230025

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Di Barba, Paolo | Januszkiewicz, Łukasz

Article Type: Research Article

Abstract: In modern wireless telecommunication systems, antenna arrays are widely used as elements of multiple – input multiple – output technology. In the fifth-generation systems, arrays are utilized to realize beamforming that forms the radiation pattern of the base station in the direction of the mobile user. This requires the utilization of many-element antenna arrays that are precisely controlled to achieve the required radiation properties. In this paper we apply the concept of deep neural network to model antenna array radiation properties. In this proof-of-concept research we aim at investigating to what extent it is possible to use deep neural networks …for modeling antenna arrays. We consider a full-wave model of linear array with a reflector, which was controlled by the phase and amplitude of the signals feeding the elementary radiators. The applied method made it possible to solve the direct and inverse problems. The results that we obtained show that deep neural networks are able to model antenna array properties. Show more

Keywords: Deep neural networks, antenna array, radiation pattern, method of moments

DOI: 10.3233/JAE-230086

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Di Barba, Paolo | Dughiero, Fabrizio | Forzan, Michele | Lowther, David A. | Marconi, Antonio | Mognaschi, Maria Evelina | Sykulski, Jan K.

Article Type: Research Article

Abstract: The authors explore the possibility of applying a convolutional Naeural Network (CNN) to the solution of coupled electromagnetic and thermal problem, focusing on the classical problem of induction heating systems, traditionally solved by resorting to Finite Element (FE) models. In fact, FE modelling is widely used in the design of induction heating systems due its accuracy, even if the solution of a coupled nonlinear problem is expensive in terms of computational time and hardware resources, notably in 3D analysis. A model based on CNN could be an interesting alternative; in fact, CNN is a learning model selected for its …excellent ability to converge, even when trained with a limited dataset. CNNs are able to treat images as input and they are used here as follows: given a temperature map in the workpiece, identify the corresponding vector of current, frequency and process heating time; this mapping is a model of the inverse induction heating problem. Specifically, we consider as an example the induction heating of a cylindrical steel billet, made of C45 steel, placed in a solenoidal inductor coil exhibiting the same axial length of the billet (TEAM 36 problem). A thorough heating process is usually applied before hot working of the billet, as in an extrusion process, but this methodology can be applied also in the design of induction hardening processes. First, a CNN has been trained from scratch by means of a dataset of FE solutions of coupled electromagnetic and thermal problems. For the sake of a comparison, a transfer learning technique is applied using GoogLeNet, i.e. a Deep Convolutional Neural Network able to classify images: starting from the pre-trained GoogLeNet, its training has been subsequently refined with the dataset of solutions from FE analyses. When the training dataset contains a limited number of samples, GoogleNet shows good accuracy in predicting the process parameters; in the case of a high number of samples in the training set, namely beyond a threshold like e.g. 1500, both CNNs show good accuracy of the result. Show more

Keywords: Numerical modelling, coupled fields, neural network, induction heating, finite-element analysis

DOI: 10.3233/JAE-230087

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Stamou, Georgia | Angelopoulos, Spyridon | Hristoforou, Evangelos

Article Type: Research Article

Abstract: This paper presents a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for Steel Health Monitoring. The system operates by detecting magnetic anomalies in ferromagnetic materials caused by strain, corrosion, etc. This sensor can have various applications in the transportation, building, and aerospace fields for safety and maintenance monitoring of ferromagnetic materials. In this work, a low-cost device, that combines a high-sensitivity AMR sensor, a microcontroller, and supporting electronics has been designed and implemented. This sensor allows the contactless measurement of the magnetic flux density along three axes, when placed above the material under test, while the microcontroller and …the required electronics enable real-time analysis and monitoring of measurements. In order to house and protect the sensor under various circumstances, a 3D-printed enclosure has also been created. This device can be used along with rehabilitation techniques for treatment of defective areas of an under-test material. Its versatility allows it to be employed in a variety of testing conditions for both single-point and scanning mode monitoring. The device’s portability, ease of use and applicability to on-site measurements make it accessible to a wide range of users, requiring only a personal computer to display the measurements. Finally, measurements are presented to prove the device’s accuracy for steel health monitoring. Show more

Keywords: Anisotropic Magnetoresistance (AMR), magnetic sensor, steel heath monitoring, microcontroller, magnetic anomaly detection

DOI: 10.3233/JAE-230137

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-6, 2023

Authors: Baskaran, Prashanth | Ramos, Helena Geirinhas | Ribeiro, Artur Lopes

Article Type: Research Article

Abstract: Probability of Detection (PoD) models, in general, take into consideration one or multiple flaw parameters such as its length, maximum depth, and/or maximum surface area, and one flaw signal. However, due to correlation between the response signals, it might be necessary to consider multiple flaw response signals simultaneously. Hence, in this work, we demonstrate the possibility of including multiple correlated flaw signals, features, towards the construction of a PoD curve. The flaw features considered are the 3 components of the magnetic flux density. This is a simulation based PoD estimation for a narrow opening notch type flaw located in the …sub-surface of a two-layer geometry. The inspection is carried out by an uniform eddy current probe that induces a spatially uniform fields into the conducting space, around the region of interest. The analysis was performed using the semi-analytical boundary element method (BEM). Show more

Keywords: Boundary element method (BEM), electric dipoles, magnetic-electric Green’s functions, Probability of Detection (PoD)

DOI: 10.3233/JAE-230134

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-7, 2023

Authors: Yu, Zhiqing | Zhao, Jianhui | Wei, Rongqiang

Article Type: Research Article

Abstract: The dynamic response and operational reliability of high-speed solenoid valve (HSV) for diesel engine injector are the main indicators to measure their performance. At high-frequency, the eddy current energy and Joule energy generated by the HSV will be converted into heat, which has a significant impact on the service life of HSV. The optimization of HSV involves the interaction between energy loss and the dynamic response of HSV. To optimize the HSV dynamic response time considering energy loss, the HSV work process simulation model was established in this paper, and the model was verified based on armature lift experimental data. …Without changing the structural parameters of the HSV, the four parameters of electroconductibility, spring stiffness, damping coefficient, and coil resistance were selected as the key parameters affecting the dynamic response and energy loss. The response surface models (RSMs) of opening response time, closing response time, eddy current energy and Joule energy of the HSV were constructed by using the smoothing spline-analysis of variance method. The multi-objective cooperation optimization of HSV under the interaction of dynamic response characteristics and energy loss was completed by using non-dominated sorting genetic algorithms. After optimization, the opening and closing response times of HSV were reduced by 15.1% and 16.6% respectively, while the eddy current energy and Joule energy were reduced by 5.2% and 48.4% respectively. In this paper, the dynamic response and energy loss were jointly optimized. The presented results provide theory instruction for multi-objective cooperative optimization of HSV. Show more

Keywords: High-speed solenoid valve, dynamic response, energy loss, NSGA-II, multi-objective cooperation optimization

DOI: 10.3233/JAE-230099

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Lowther, David A.

Article Type: Review Article

Abstract: Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at …the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades. Show more

Keywords: Electrical machine design, machine learning, key performance indicators, material properties, optimization

DOI: 10.3233/JAE-230104

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Jiang, Shanyi | Pang, Xinliang | Chang, Yunfen | Cui, Jie | Han, Yubing

Article Type: Research Article

Abstract: In this study, we investigated the coupling features of the nuclear electromagnetic pulse (NEMP) on overhead cables in the middle-and-far regions, different from the transmission line model commonly used for field-line coupling in high-frequency cases, using a simpler lumped approximation to solve the electrically small size model in low-frequency cases. To verify its effectiveness, a simulation model with the same conditions was set up using the software of Computer Simulation Technology (CST), and cable coupling experiments were performed in a laboratory environment using a bounded-wave electromagnetic pulse simulator. The calculated results of the lumped approximation circuit were compared with the …CST simulation and measured results, and the agreement was good. The results also shows that the load exhibits a differential response in the case of the low impedance and it is consistent with the excitation signal in the case of the high impedance. Finally, some more experiments were constructed to analyzed the effect of different cable parameters on the cable load response through experiments, and the experimental results are also in general agreement with the theoretical analysis, in which the induced signal of the low-impedance load is mainly determined by the magnetic field in the direction normal to the cable and the ground loop and the induced signal of the high-impedance load is mainly determined by the electric field in the direction of the height of the cable erection. Show more

Keywords: Nuclear electromagnetic pulse (NEMP) in the middle-and-far regions, cable coupling, electrically small size model, bounded-wave simulator experiments, digital integral

DOI: 10.3233/JAE-230010

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Liu, Qing | Ge, Ruihuan | Wang, Li | Ren, Tianming | Feng, Ming

Article Type: Research Article

Abstract: A single-structured hybrid gas-magnetic bearing (HGMB) has been proposed for frequent start/stop occasions, which eliminates foil structures or static pressure systems by using the closed magnetic poles of the active magnetic bearing (AMB) as the bushing of the gas bearing. This allows the proposed bearing to realize the functions of both AMB and gas bearing with a single bearing structure. In this paper, the bias currents of AMB, aimed for enhanced load capacity and dynamic characteristics, are omitted to reduce power consumption and heat. The combination of zero-bias AMB and rigid self-acting gas bearing in a single bearing structure is …therefore proposed. The rotor orbits of gas bearing, AMB, single-structured HGMB, and single-structured zero-bias HGMB in conditions of varied horizontal and vertical external loads are simulated. The dynamic performances during the run-up processes of AMB, HGMB, and zero-bias HGMB are investigated. The electromagnetic forces of each kind of bearing are compared. Numerical results demonstrate that by applying the single-structured zero-bias HGMB, the power consumption can be significantly reduced in contrast with pure AMB and single-structured HGMB. The reduced load capacity and dynamic characteristics of zero-bias AMB can be compensated by the rigid self-acting gas bearing, making the single-structured zero-bias HGMB an ideal candidate for cryogenic, ultra-high speed as well as frequent start/stop occasions. Show more

Keywords: Single-structured hybrid gas-magnetic bearing, zero-bias AMB, rotordynamic performance, frequent start/stops

DOI: 10.3233/JAE-230042

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Silue, Dozohoua | Labidi, Mondher | Choubani, Fethi

Article Type: Review Article

Abstract: In this paper, a small antenna is proposed to diagnose skin sarcoma at an embryonic stage. The antenna has an area of 30.54 × 15.27 mm2 and resonates at 1429 MHz with a reflection coefficient of −17.64 dB. The structure consists of a 35 μm copper sheet etched on a 1.6 mm FR-4 substrate. The diagnosis is based on the resonance frequency shift, and the SAR (Specific Absorption Rate) variation when the antenna is positioned on malignant tissue. For the simulations, a three-layer body phantom (skin, fat, muscle), and a half-sphere tumor phantom were considered. Simulations of the antenna performances showed that for a …tumor of 100 μm, the resonant frequency, and the SAR decrease by 2 MHz, and 1.09 mW/Kg, respectively. In addition to sarcoma detection, the antenna’s 3.6 dBi gain allows for 124.47 m biomedical communication links in a complex environment. Show more

Keywords: Small antenna, early cancer diagnosis, skin sarcoma, frequency shift detection, scale of cancer diagnosis

DOI: 10.3233/JAE-230048

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. Pre-press, no. Pre-press, pp. 1-14, 2023