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Статті в журналах з теми "Magnetic Model Identification"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Shabani, R., S. Tariverdilo, and H. Salarieh. "Nonlinear identification of electro-magnetic force model." Journal of Zhejiang University SCIENCE A 11, no. 3 (February 12, 2010): 165–74. http://dx.doi.org/10.1631/jzus.a0900203.

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2

Va´zquez, J. A., E. H. Maslen, H. J. Ahn, and D. C. Han. "Model Identification of a Rotor With Magnetic Bearings." Journal of Engineering for Gas Turbines and Power 125, no. 1 (December 27, 2002): 149–55. http://dx.doi.org/10.1115/1.1499730.

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Анотація:

The experimental identification of a long flexible rotor with three magnetic bearing journals is presented. Frequency response functions are measured between the magnetic bearing journals and the sensor locations while the rotor is suspended horizontally with piano wire. These frequency response functions are compared with the responses of a rotor model and a reconciliation process is used to reduce the discrepancies between the model and the measured data. In this identification, the wire and the fit of the magnetic bearing journals are identified as the sources of model error. As a result of the reconciliation process, equivalent dynamic stiffness are calculated for the piano wire and the fit of the magnetic bearing journals. Several significant numeral issues that were encountered during the process are discussed and solutions to some of these problems are presented.

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3

Lin, C. E., and H. L. Jou. "Force model identification for magnetic suspension systems via magnetic field measurement." IEEE Transactions on Instrumentation and Measurement 42, no. 3 (June 1993): 767–71. http://dx.doi.org/10.1109/19.231612.

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4

Rugkwamsook, P., and C. E. Korman. "Identification of magnetic aftereffect model parameters: Temperature dependence." IEEE Transactions on Magnetics 34, no. 4 (July 1998): 1863–65. http://dx.doi.org/10.1109/20.706728.

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5

Armando, Eric, Radu Iustin Bojoi, Paolo Guglielmi, Gianmario Pellegrino, and Michele Pastorelli. "Experimental Identification of the Magnetic Model of Synchronous Machines." IEEE Transactions on Industry Applications 49, no. 5 (September 2013): 2116–25. http://dx.doi.org/10.1109/tia.2013.2258876.

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6

Pellegrino, Gianmario, Barbara Boazzo, and Thomas M. Jahns. "Magnetic Model Self-Identification for PM Synchronous Machine Drives." IEEE Transactions on Industry Applications 51, no. 3 (May 2015): 2246–54. http://dx.doi.org/10.1109/tia.2014.2365627.

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7

Hall, Sebastian, Francisco J. Marquez-Fernandez, and Mats Alakula. "Dynamic Magnetic Model Identification of Permanent Magnet Synchronous Machines." IEEE Transactions on Energy Conversion 32, no. 4 (December 2017): 1367–75. http://dx.doi.org/10.1109/tec.2017.2704114.

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8

Ziolkowski, Marek, Hartmut Brauer, and Milko Kuilekov. "Interface identification in magnetic fluid dynamics." Serbian Journal of Electrical Engineering 1, no. 1 (2003): 61–69. http://dx.doi.org/10.2298/sjee0301061z.

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Анотація:

In magnetic fluid dynamics appears the problem of reconstruction of free boundary between conducting fluids, e.g. in aluminum electrolysis cells. We have investigated how the interface between two fluids of different conductivity of a highly simplified model of an aluminum electrolysis cell could be reconstructed by means of external magnetic field measurements using simple genetic algorithm.

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9

Li, Guoxin, Zongli Lin, Paul E. Allaire, and Jihao Luo. "Modeling of a High Speed Rotor Test Rig With Active Magnetic Bearings." Journal of Vibration and Acoustics 128, no. 3 (December 2, 2005): 269–81. http://dx.doi.org/10.1115/1.2172254.

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Анотація:

This paper reports on the modeling and experimental identification of a high speed rotor-magnetic bearing test rig. An accurate nominal model and an uncertainty representation are developed for robust controller synthesis and analysis. A combination of analytical modeling, model updating, and identification is employed for each system component and for the system as a whole. This approach takes advantage of both the behavior modeling and input/output modeling methods. Analytical models of the rotor and the magnetic bearings are first developed from physical laws and refined by comparison with the experimental data. The substructure model is directly identified from the experimental data by a structured identification approach. Models of the electronic systems, such as the filters, amplifiers, sensors, and digital controller, are developed through experimental identification. These component models are then assembled to obtain the overall system model. Closed-loop tests are conducted to identify parameters in the model. Advanced control techniques based on H∞ control and μ synthesis are developed and successfully implemented on the test rig, which further validates the model.

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10

Mofidian, S. M. Mahdi, and Hamzeh Bardaweel. "Theoretical study and experimental identification of elastic-magnetic vibration isolation system." Journal of Intelligent Material Systems and Structures 29, no. 18 (July 10, 2018): 3550–61. http://dx.doi.org/10.1177/1045389x18783869.

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Анотація:

A vibration isolation system featuring a combination of elastic and magnetic springs and viscous and magnetic damping is presented. A mechanical flat spring houses a permanent magnet that is levitated between two stationary magnets. A prototype of the isolator is manufactured. COMSOL models are developed for the mechanical and magnetic springs. Measured data and model simulations show that the magnets arrangement results in nonlinear magnetic spring with negative linear stiffness. The mechanical spring exhibits linear behavior with positive stiffness. Experiments are performed and a nonlinear dynamic model is developed. The fabricated isolator is characterized at low and high acceleration levels. Results from model show good agreement with measured data at lower acceleration levels. Slight mismatch between model and experiment is evident at higher accelerations. This mismatch is due to the existence of lateral vibrations that are not accounted for in the unidirectional model. Results show that the combination of mechanical flat spring and magnetic spring reduces the resonant frequency of the isolator. In addition, results confirm the ability of the isolator to attenuate vibrations higher than 11.91 Hz when excited at 2.4525 [m s−2].

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11

Varvolik, Vasyl, Shuo Wang, Dmytro Prystupa, Giampaolo Buticchi, Sergei Peresada, Michael Galea, and Serhiy Bozhko. "Fast Experimental Magnetic Model Identification for Synchronous Reluctance Motor Drives." Energies 15, no. 6 (March 17, 2022): 2207. http://dx.doi.org/10.3390/en15062207.

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Анотація:

The accurate magnetic model is mandatory for high-performance control of high anisotropy synchronous machines. This paper presents a time-efficient and accurate magnetic model identification based on triangle current injection while the machine under the test is driven at a constant speed by a prime mover. The current injection pattern allows scanning the whole range of current, reducing the identification time compared to the standard constant-speed method (CSM) with the same level of accuracy. The ohmic voltage drop and inverter nonlinearities are compensated by using the average voltage of motor and generator modes. The synchronous reluctance machine is used as a case study for validation through the comparison between the experimental results obtained by the proposed method and the CSM against finite element simulation. Moreover, the temperature variation of the machine winding is measured showing no considerable changes during the identification test.

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12

Honc, Daniel, and Eleonora Riva Sanseverino. "Magnetic Levitation – Modelling, Identification and Open Loop Verification." TRANSACTIONS ON ELECTRICAL ENGINEERING 8, no. 1 (March 30, 2020): 13–16. http://dx.doi.org/10.14311/tee.2019.1.013.

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Анотація:

<p>The paper describes a procedure using the first principle modelling and experimental identification of the Magnetic Levitation Model CE 152. It is a modified version of the paper [1]. The difference is that the identification and verification is done in open loop and constraints logic is added in the current paper. The author optimized and simplified dynamic model to a minimum to what is needed to characterize given system for the simulation and control design purposes. Only few open-loop experiments are needed to estimate the unknown parameters. Model quality is verified in open loop where the real and simulated data are compared. The model can serve as a simulation model for some standard control algorithms or as a process model for advanced control method design.</p>

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13

Longhitano, Maria Roberta, Fabien Sixdenier, Riccardo Scorretti, Laurent Krähenbühl, and Christophe Geuzaine. "Temperature-dependent hysteresis model for soft magnetic materials." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 5 (September 2, 2019): 1595–613. http://dx.doi.org/10.1108/compel-12-2018-0535.

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Анотація:

Purpose To understand the behavior of the magnetization processes in ferromagnetic materials in function of temperature, a temperature-dependent hysteresis model is necessary. This study aims to investigate how temperature can be accounted for in the energy-based hysteresis model, via an appropriate parameter identification and interpolation procedure. Design/methodology/approach The hysteresis model used for simulating the material response is energy-consistent and relies on thermodynamic principles. The material parameters have been identified by unidirectional alternating measurements, and the model has been tested for both simple and complex excitation waveforms. Measurements and simulations have been performed on a soft ferrite toroidal sample characterized in a wide temperature range. Findings The analysis shows that the model is able to represent accurately arbitrary excitation waveforms in function of temperature. The identification method used to determine the model parameters has proven its robustness: starting from simple excitation waveforms, the complex ones can be simulated precisely. Research limitations/implications As parameters vary depending on temperature, a new parameter variation law in function of temperature has been proposed. Practical implications A complete static hysteresis model able to take the temperature into account is now available. The identification is quite simple and requires very few measurements at different temperatures. Originality/value The results suggest that it is possible to predict magnetization curves within the measured range, starting from a reduced set of measured data.

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14

Radhi, Muna Abdul Hussain. "Human Identification Model Considering Biometrics Features." Journal La Multiapp 3, no. 4 (August 26, 2022): 198–206. http://dx.doi.org/10.37899/journallamultiapp.v3i4.692.

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In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.

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15

Yang, Bei Bei, Ye Fa Hu, and Guo Ping Ding. "System Identification Based on Recursive Least Square Method for the Magnetic Suspension Active Vibration Isolation System." Applied Mechanics and Materials 150 (January 2012): 105–10. http://dx.doi.org/10.4028/www.scientific.net/amm.150.105.

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Анотація:

In this paper, we use recursive least squares method for magnetic single layer vibration isolation system identification to get the system transfer function matrix. By considering the fitting degree, pole-zero, the step response to adjust the order of model and noise structure for optimizing the model Identification. Applying the system transfer function matrix to the magnetic active vibration control system to improve the isolation effect. The results showed that: significantly improved isolation effect, verify the validity of this identification model for magnetic single isolation system.

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16

Jin, Junjie, Zhenyun Duan, Feng Sun, Qing Li, Pengpeng Xia, Jiaqi Jin, and Koichi Oka. "Model identification and analysis for parallel permanent magnetic suspension system based on ARX model." International Journal of Applied Electromagnetics and Mechanics 52, no. 1-2 (December 29, 2016): 145–52. http://dx.doi.org/10.3233/jae-162027.

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17

Lin, Dingsheng, Ping Zhou, and Anders Bergqvist. "Improved Vector Play Model and Parameter Identification for Magnetic Hysteresis Materials." IEEE Transactions on Magnetics 50, no. 2 (February 2014): 357–60. http://dx.doi.org/10.1109/tmag.2013.2281567.

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18

Ortombina, Ludovico, Dario Pasqualotto, Fabio Tinazzi, and Mauro Zigliotto. "Magnetic Model Identification of Synchronous Motors Considering Speed and Load Transients." IEEE Transactions on Industry Applications 56, no. 5 (September 2020): 4945–54. http://dx.doi.org/10.1109/tia.2020.3003555.

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19

Korman, C. E., and P. Rugkwamsook. "Identification of magnetic aftereffect model parameters: comparison of experiment and simulations." IEEE Transactions on Magnetics 33, no. 5 (1997): 4176–78. http://dx.doi.org/10.1109/20.619701.

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20

Gruosso, G., and M. Repetto. "Identification and simulation of a circuit‐based model of magnetic hysteresis." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 24, no. 2 (June 2005): 534–45. http://dx.doi.org/10.1108/03321640510586150.

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21

Yu, Lizhong, and Weiguo Zhang. "Quantitative approach to sediment source identification by using magnetic diagnosis model." Chinese Science Bulletin 44, no. 6 (March 1999): 504–10. http://dx.doi.org/10.1007/bf02885535.

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22

Ben Gharsallah, Mohamed, and Ezzedine Ben Braiek. "Defect identification in magnetic tile images using an improved nonlinear diffusion method." Transactions of the Institute of Measurement and Control 43, no. 11 (January 24, 2021): 2413–24. http://dx.doi.org/10.1177/0142331220982220.

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Visual inspection of surface defects is a crucial step in the magnetic tile manufacturing process. Magnetic tile images suffer from a non-uniform illumination, texture and noise that disperse irregularly in flawless image areas. As a result, common edge detection and threshold segmentation techniques fail to identify these kinds of defects. In this work, we present a robust algorithm for defect identification in magnetic tile images. The proposed method is based on a new anisotropic diffusion filtering model. Unlike traditional anisotropic diffusion models that take into account only gradient magnitude information, the proposed model combines together gradient magnitude and a new local difference image feature. The aim is to remove bright shapes and undesirable artifacts in the faultless region in magnetic tile images. In addition, the method activates a smoothing process in the flawless region to homogenize the background and simultaneously a sharpening in the defect boundaries to highlight anomalies. Experimental results on a number of magnetic tiles samples containing different types of defects have demonstrated the efficiency of the proposed diffusion method.

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23

Kaleta, Jerzy, Daniel Lewandowski, and Piotr Zając. "Experimental Identification of Magnetorheological Composites and Elastomers Properties." Materials Science Forum 482 (April 2005): 403–6. http://dx.doi.org/10.4028/www.scientific.net/msf.482.403.

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The paper presents measurement and results of mathematical modelling of damping in cyclic loaded magnetorheological composites - MRC. The original experimental set-up for cyclic shearing mode has been created. Examples of possible applications, measurement and processing mechanical signals (shear stress - T, deformation - g) and magnetic signals (strength of magnetic field - H) were provided. For the modelling four-parameters viscoelastic-viscoplastic model was chosen. Result of identification confirmed an appropriate choice of the model.

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24

PORKUIAN, Olga, Vladimir MORKUN, Natalia MORKUN, and Irina GAPONENKO. "THE INFLUENCE OF THE CHARACTERISTICS VARIATIONS OF THE CONCENTRATING PLANT CONTROL OBJECT ON THE IDENTIFICATION RESULTS USING THE HAMMERSTEIN MODEL." Sustainable Development of Mountain Territories 13, no. 1 (March 27, 2021): 94–102. http://dx.doi.org/10.21177/1998-4502-2021-13-1-94-102.

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As a result of the identification based on the Hammerstein model of objects of the first stage of iron ore magnetic separation, the adequacy of the model is obtained. All results of the testing of the developed identification algorithms show that the subsystem of identification of the automated process control systems of processing plants based on the Hammerstein hybrid model allows to carry out satisfactory identification of objects and, as a consequence, to improve the quality of technological processes. The study of the influence of the coefficient of various typical links on the results of identification using orthogonal parallel and parallel-recursive Hammerstein models showed that these models allow considering the differences in the properties of identifiable objects adequately.

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25

Dong, Xiaomin, Chi Duan, and Jianqiang Yu. "Research on chain-model transition identification of magnetic dipole theory for magneto-rheological fluid." Advances in Mechanical Engineering 8, no. 12 (December 2016): 168781401668363. http://dx.doi.org/10.1177/1687814016683636.

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Анотація:

Chain formation model is very useful to characterize the magneto-rheological phenomenon and prepare good magneto-rheological fluids. The single-chain model is common to explain the process of chain formation for ferromagnetic particles under magnetic field. With the increment of magnetic field and ferromagnetic particle content, the chain will transit from the single chain to multi-chains. However, there are few literatures involved in this phenomenon. This study investigates the effect of magnetic field and ferromagnetic particles content on the transition. The static yield stresses at different magnetic fields were measured under quasi-static mode for different magneto-rheological fluid samples. The results show that the transition of chain model can be identified on two parameters including the amplitude of static yield angle, [Formula: see text], and the inclined angles distribution, [Formula: see text]. The single-chain model is only effective under low magnetic field and ferromagnetic particles below content of 30% volume. With the increment of magnetic field and ferromagnetic particles content, the transition from single chain to multiple chains will be observed, which validates that there is a transition from the single chain to multi-chains.

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26

Wang, Anming, Jianjun Meng, Ruxun Xu, and Decang Li. "Parameter Identification and Linear Model of Giant Magnetostrictive Vibrator." Discrete Dynamics in Nature and Society 2021 (March 15, 2021): 1–15. http://dx.doi.org/10.1155/2021/6676911.

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Анотація:

A linear magnetization model is built to replace the Jiles–Atherton model in order to describe the relationship between the magnetic field intensity and the magnetization intensity of the giant magnetostrictive vibrator (GMV). The systematic modeling of the GMV is composed of three aspects, i.e., the structural mechanic model, the magnetostrictive model, and the Jiles–Atherton model. The Jiles–Atherton model has five parameters to be defined; hence, its solution is so complex that it is not convenient in application. Therefore, the immune genetic algorithm (IGA) is applied in the identification of the five parameters of the Jiles–Atherton model and it showed a higher stability compared with the identification result of the differential evolution algorithm (DEA). The identification parameters of the two algorithms were employed, respectively, to calculate the excitation force and it was found that the relative error of IGA was evidently smaller than that of DEA, indicating that the former was more reliable than the latter. According to the identification results of IGA and based on the least square method (LSM), curve-fittings to the magnetic field intensity and magnetization intensity were conducted by using the linear function. And the linear magnetization model was built to replace the Jiles–Atherton model. Research results show that the linear model of the GMV can be established by combining the linear magnetization model with the structural mechanic model as well as the giant magnetostrictive model. The linear magnetization model, which has great engineering application value, can be applied in the open-loop control of the vibrator.

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27

Chiu, Hsin-Lin. "Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic-Bearing System." Applied Sciences 12, no. 17 (August 26, 2022): 8556. http://dx.doi.org/10.3390/app12178556.

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A systematic identification approach for the rotor/radial active magnetic bearing (rotor/RAMB) system is presented in this study. First, the system identification of the controller of commercial TMP is undertaken, and the corresponding linear dynamic models are constructed. To perfectly excite the nonlinearities of the rotor/RAMB system, a parallel amplitude-modulated pseudo-random binary sequence (PAPRBS) generator, which possesses the merits of no correlation among the perturbation signals, is employed. The dynamics of the rotor/RAMB system is identified with a Hammerstein–Wiener model. To reduce the difficulty of the identified two nonlinear blocks, the output nonlinear characteristics are estimated prior to the recursive process. Two conventional nonlinear model structures, i.e., NARX and NARMAX, are employed for comparison to verify the effectiveness of the identified Hammerstein–Wiener model. The averaged fit values of the Hammerstein–Wiener model, NARX model, and NARMAX model are 93.25%, 88.36%, and 76.91%, respectively.

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28

Kuczmann, Miklós. "Dynamic Preisach model identification applying FEM and measured BH curve." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 6 (October 28, 2014): 2043–52. http://dx.doi.org/10.1108/compel-11-2013-0368.

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Анотація:

Purpose – The purpose of this paper is to develop a viscous-type frequency dependent scalar Preisach hysteresis model and to identify the model using measured data and nonlinear numerical field analysis. The hysteresis model must be fast and well applicable in electromagnetic field simulations. Design/methodology/approach – Iron parts of electrical machines are made of non-oriented isotropic ferromagnetic materials. The finite element method (FEM) is usually applied in the numerical field analysis and design of this equipment. The scalar Preisach hysteresis model has been implemented for the simulation of static and dynamic magnetic effects inside the ferromagnetic parts of different electrical equipment. Findings – The comparison between measured and simulated data using a toroidal core shows a good agreement. A modified nonlinear version of TEAM Problem No. 30.a is also shown to test the hysteresis model in the FEM procedure. Originality/value – The dynamic model is an extension of the static one; an extra magnetic field intensity term is added to the output of the static inverse model. This is a viscosity-type dynamic model. The fixed-point method with stable scheme has been realized to take frequency dependent anomalous losses into account in FEM. This scheme can be used efficiently in the frame of any potential formulations of Maxwell's equations.

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29

Wang, Bowei. "Convolutional Neural Network Based on Brain Tumor Identification and Classification." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 453–60. http://dx.doi.org/10.54097/hset.v16i.2611.

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Анотація:

The human brain is one of the body's most major organs. If there are problems within the human brain, they may cause serious consequences, and even endangers the human life. One of the most fatal diseases for humans is a brain tumor. In the old days, tumor detection was done manually by doctors through reading magnetic resonance images, which might not be time efficient, and sometimes may even produce inaccurate results. Nowadays, with the development of science and technology, Artificial Intelligence (AI) is present in many fields in human life, including medical field. Tumor detection with AI is one of the applications that technology changes human life. The Convolution Neural Network (CNN), a prominent algorithm in deep learning, is widely employed in tumor identification. In this study, a CNN model is proposed. Over 7000 brain tumor magnetic resonance images, including glioma, meningioma, no tumor and pituitary are used in this study. The images are also preprocessed to improve the accuracy of the proposed models. In this study, the well-known VGG16 model, which is a pretrained deep learning model, is utilized to compare with the proposed model. The proposed model and the VGG16 model are trained and evaluated using both the original (uncropped) images and preprocessed (cropped) images. The results of the experiment indicate that the suggested model exceeds the VGG16 model in the light of loss and accuracy.

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30

Rasilo, Paavo, Deepak Singh, Juha Jeronen, Ugur Aydin, Floran Martin, Anouar Belahcen, Laurent Daniel, and Reijo Kouhia. "Flexible identification procedure for thermodynamic constitutive models for magnetostrictive materials." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2223 (March 2019): 20180280. http://dx.doi.org/10.1098/rspa.2018.0280.

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Анотація:

We present a novel approach for identifying a multiaxial thermodynamic magneto-mechanical constitutive law by direct bi- or trivariate spline interpolation from available magnetization and magnetostriction data. Reference data are first produced with a multiscale model in the case of a magnetic field and uniaxial and shear stresses. The thermodynamic model fits well to the results of the multiscale model, after which the models are compared under complex multiaxial loadings. A surprisingly good agreement between the two models is found, but some differences in the magnetostrictive behaviour are also pointed out. Finally, the model is fitted to measurement results from an electrical steel sheet. The spline-based constitutive law overcomes several drawbacks of analytical approaches used earlier. The presented models and measurement results are openly available.

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31

Afonin, Igor L., Alexander L. Polyakov, Yury N. Tyschuk, Vladislav V. Golovin, and Gennady V. Slezkin. "Mathematical model for spacecrafts identification." Radioelectronics. Nanosystems. Information Technologies. 14, no. 2 (June 30, 2022): 111–18. http://dx.doi.org/10.17725/rensit.2022.14.111.

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Proposed below is a mathematical model for reception and processing of spurious (“uncontrolled” radiation) from constantly operating units of the special complex installed onboard a spacecraft. This model makes it possible to implement a new technique for identification of such radiation thus improving the capabilities of the space monitoring. At the same time creation of stand-alone radio systems solely for identification of the spacecrafts based on the proposed mathematic model would require significant expenses therefore it is worthwhile to add new identification hardware to the existing ground-based radio systems used for space flight control. Identifying features in the proposed mathematical model are the parameters of signals of the uncontrolled radiation from constantly operating units installed onboard spacecrafts that “leak” through antenna systems (master generators, heterodyne oscillators in the spacecraft radio receivers). Identification of spacecrafts by uncontrolled radiation from the receiving master generators and heterodyne oscillators involves keeping track of the behavior of oscillation parameters as well as identifying signs that distinguish the oscillations of one generator from another. Since the uncontrolled radiation from heterodyne oscillators is a harmonic oscillation, it features such parameters as amplitude, frequency and initial phase. It is impossible to use the amplitude and initial phase of the signal for identification purposes because the propagation medium strongly affects these parameters. The most informative for identification purposes is the frequency of oscillations, or rather, the behavior of the frequency changes over time. These changes are due to the frequency instability of the onboard master generators. The behavior of the frequency change depends on the characteristics of each onboard generator, which serves as a basis for identification. It should be noted that the identification process can be conditionally divided into two stages: the first stage includes validation of models and processing (evaluation) algorithms while the second stage involves classification of the results of processing (evaluation).

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Amir, Mounir, Mourad Zergoug, and Aissa Amrouche. "Identification Parameters with Neural Network for Preisach Hysteresis Model." Applied Mechanics and Materials 541-542 (March 2014): 487–93. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.487.

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The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of devices, the proposed approach has been applied to model the behavior of many samples and the results show the robustness and efficiency of Neural Network to model the phenomenon of hysteresis loop. The goal of this study is to optimize the parameters of hysteresis Loop by Preisach model with the Neural Network, the method developed is based on an analysis of two distribution functions. The modified Lorentzian function and Gaussian function have been analyzed. The implemented software and performances of the distributions are presented.

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33

Singh, Sandeep, and Rajiv Tiwari. "Model-based fatigue crack identification in rotors integrated with active magnetic bearings." Journal of Vibration and Control 23, no. 6 (August 9, 2016): 980–1000. http://dx.doi.org/10.1177/1077546315587146.

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34

Rao, C. Sankar, and M. Chidambaram. "Subspace Identification of Unstable Transfer Function Model for a Magnetic Levitation System." IFAC Proceedings Volumes 47, no. 1 (2014): 394–99. http://dx.doi.org/10.3182/20140313-3-in-3024.00104.

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35

Sedira, D., Y. Gabi, A. Kedous-Lebouc, K. Jacob, B. Wolter, and B. Straß. "ABC method for hysteresis model parameters identification." Journal of Magnetism and Magnetic Materials 505 (July 2020): 166724. http://dx.doi.org/10.1016/j.jmmm.2020.166724.

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36

Szabó, Zs, I. Tugyi, Gy Kádár, and J. Füzi. "Identification procedures for scalar Preisach model." Physica B: Condensed Matter 343, no. 1-4 (January 2004): 142–47. http://dx.doi.org/10.1016/j.physb.2003.08.086.

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37

Ren, Linjie, Guobin Lin, Yuanzhe Zhao, and Zhiming Liao. "Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model." Sustainability 13, no. 8 (April 14, 2021): 4379. http://dx.doi.org/10.3390/su13084379.

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In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms.

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38

Canova, Aldo, Fabio Freschi, Luca Giaccone, Maurizio Repetto, and Luigi Solimene. "Identification of Material Properties and Optimal Design of Magnetically Shielded Rooms." Magnetochemistry 7, no. 2 (February 6, 2021): 23. http://dx.doi.org/10.3390/magnetochemistry7020023.

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In this paper, we propose an optimal design procedure for magnetically shielded rooms. Focusing on multi-layer ferromagnetic structures, where inner layers operate at very low magnetic field, we propose an identification method of the magnetic material characteristic in the Rayleigh region. A numerical model to simulate the shielding efficiency of a multi-layer ferromagnetic structure is presented and experimentally tested on different geometries and layer configurations. The fixed point iterative method is adopted to handle the nonlinearity of the magnetic material. In conclusion, the optimization of the design parameters of a MSR is discussed, using the Vector Immune System algorithm to minimize the magnetic field inside the room and the cost of the structure. The results highlight that a linear magnetic characteristic for the material is sufficient to identify the suitable geometry of the shield, but the nonlinear model in the Rayleigh region is of fundamental importance to determine a realistic shielding factor.

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39

Ullah, Zia, Xinhua Wang, Yingchun Chen, Tao Zhang, Haiyang Ju, and Yizhen Zhao. "Time-Domain Output Data Identification Model for Pipeline Flaw Detection Using Blind Source Separation Technique Complexity Pursuit." Acoustics 1, no. 1 (February 19, 2019): 199–219. http://dx.doi.org/10.3390/acoustics1010013.

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Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal data. This paper discusses such output-only modal identification method Complexity Pursuit (CP) based on blind signal separation. An application to the pipeline flaw detection is presented and it is shown that the complexity pursuit algorithm blindly estimates the modal parameters from the measured magnetic field signals. Numerical simulations for multi-degree of freedom systems show that the method can precisely identify the structural parameters. Experiments are performed first in a controlled laboratory environment secondly in real world, on pipeline magnetic field data, recorded using high precision magnetic field sensors. The measured structural responses are given as input to the blind source separation model where the complexity pursuit algorithm blindly extracted the least complex signals from the observed mixtures that were guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses exhibit rich physical interpretation of the pipeline structures.

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40

Lee, Hyun Chang, Min-Hung Hsiao, Jen-Kuang Huang, and Chung-Wen Chen. "Identification of Stochastic System and Controller via Projection Filters." Journal of Vibration and Acoustics 118, no. 2 (April 1, 1996): 169–76. http://dx.doi.org/10.1115/1.2889645.

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A method based on projection filters is presented for identifying an open-loop stochastic system with an existing feedback controller. The projection filters are derived from the relationship between the state-space model and the AutoRegressive with eXogeneous input (ARX) model including the system, Kalman filter and controller. Two ARX models are identified from the control input, closed-loop system response and feedback signal using least-squares method. Markov parameters of the open-loop system, Kalman filter and controller are then calculated from the coefficients of the identified ARX models. Finally, the state-space model of the open-loop stochastic system and the gain matrices for the Kalman filter and controller are realized. The method is validated by simulations and test data from an unstable large-angle magnetic suspension test facility.

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41

Fu, Hong Ya, Ping Fan Liu, Qing Chun Zhang, Guo Dong Li, and Di Bo Dong. "Structural Vibration Control of the Magnetic Flywheel Based on GA-PID Neural Network." Advanced Materials Research 605-607 (December 2012): 1120–24. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1120.

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To overcome the difficult in modeling the mode theory for magnetic flywheel, by non-parametric frequency domain identification method for identification of system model, this paper select the PID neural network nonlinear intelligent control methods based on GA. Used a method based on iterative process to suppress the resonance, combined with zero-pole theory, this research designed a GA-based PID neural network identification controller to address the modal vibration suppression problems in the external rotor flywheel .The experiments for this paper proved the effectiveness of iterative process, and the controller can restrain the system mode vibration well.

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42

You, Jiaxin, Kun Zhang, Huimin Liang, Xiangdong Feng, and Yonggang Ruan. "Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program." Actuators 10, no. 1 (January 7, 2021): 10. http://dx.doi.org/10.3390/act10010010.

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In this paper, a method for the improvement of the calculation accuracy of the distributed parameter model (DPM) of electromagnetic devices is proposed based on the kriging basis function predictive identification program (PIP). Kriging is mainly an optimal interpolation method which uses spatial self-covariance, and takes a polynomial as the basis function. The accuracy of the kriging-based surrogate model can be improved by adjusting the related functions and hyperparameters. Based on the DPM of a solenoid valve, there exist certain errors in the estimation. They can be summarized as follows: Firstly, the estimation error of magnetic flux leakage (MFL) permeance is caused directly by the deviation of the magnetic flux tube due to the segmented magnetic field line. Secondly, the estimation error of soft magnetic resistance because of the nonlinearity of the permeability of soft magnetic material leads to the change of soft magnetic resistance alongside the magnetic flux. In this paper, an improved kriging error correction method is applied to modify the leak permeance and soft magnetic resistance calculation. The kriging basis function is adjusted to adapt to the data curve of the MFL permeance error data. The calculated MFL permeance data are compared with the error variation data to select the appropriate basis function. To improve the computational efficiency, the PIP is proposed to select the appropriate basis function. The modified MFL permeance data and soft magnetic resistance are substituted into the DPM for improving the computational accuracy and efficiency of the solenoid valve.

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43

You, Jiaxin, Kun Zhang, Huimin Liang, Xiangdong Feng, and Yonggang Ruan. "Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program." Actuators 10, no. 1 (January 7, 2021): 10. http://dx.doi.org/10.3390/act10010010.

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Анотація:

In this paper, a method for the improvement of the calculation accuracy of the distributed parameter model (DPM) of electromagnetic devices is proposed based on the kriging basis function predictive identification program (PIP). Kriging is mainly an optimal interpolation method which uses spatial self-covariance, and takes a polynomial as the basis function. The accuracy of the kriging-based surrogate model can be improved by adjusting the related functions and hyperparameters. Based on the DPM of a solenoid valve, there exist certain errors in the estimation. They can be summarized as follows: Firstly, the estimation error of magnetic flux leakage (MFL) permeance is caused directly by the deviation of the magnetic flux tube due to the segmented magnetic field line. Secondly, the estimation error of soft magnetic resistance because of the nonlinearity of the permeability of soft magnetic material leads to the change of soft magnetic resistance alongside the magnetic flux. In this paper, an improved kriging error correction method is applied to modify the leak permeance and soft magnetic resistance calculation. The kriging basis function is adjusted to adapt to the data curve of the MFL permeance error data. The calculated MFL permeance data are compared with the error variation data to select the appropriate basis function. To improve the computational efficiency, the PIP is proposed to select the appropriate basis function. The modified MFL permeance data and soft magnetic resistance are substituted into the DPM for improving the computational accuracy and efficiency of the solenoid valve.

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44

Yu, Lizhong, and Frank Oldfield. "A Multivariate Mixing Model for Identifying Sediment Source from Magnetic Measurements." Quaternary Research 32, no. 2 (September 1989): 168–81. http://dx.doi.org/10.1016/0033-5894(89)90073-2.

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AbstractA sequential method for quantitative identification of sediment source components, based on magnetic measurements, has been developed and tested for sediments from the Rhode River, Maryland. Simulated mixing tests and multiple regression were employed to establish numerical relationships between source component proportions and the magnetic measurements of mixtures. On the basis of these multivariate mixing models, source components of three estuarine sediment cores were estimated by linear programming. The results strongly support the previous studies on this catchment which indicated a dramatic change in sediment source some 150 to 200 yr ago. Quantitative calculations are more useful and informative than purely qualitative descriptions.

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45

Haghgooei, Peyman, Ehsan Jamshidpour, Noureddine Takorabet, Davood Arab-Khaburi, and Babak Nahid-Mobarakeh. "Magnetic Model Identification of Wound Rotor Synchronous Machine Using a Novel Flux Estimator." IEEE Transactions on Industry Applications 57, no. 5 (September 2021): 5389–99. http://dx.doi.org/10.1109/tia.2021.3086052.

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46

Repo, A. K., P. Rasilo, A. Niemenmaa, and A. Arkkio. "Identification of Electromagnetic Torque Model for Induction Machines With Numerical Magnetic Field Solution." IEEE Transactions on Magnetics 44, no. 6 (June 2008): 1586–89. http://dx.doi.org/10.1109/tmag.2007.916143.

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47

Uffen, M. P., M. R. Krijnen, R. J. Hoogendoorn, G. J. Strijkers, V. Everts, P. I. Wuisman, and T. H. Smit. "Tissue identification with micro-magnetic resonance imaging in a caprine spinal fusion model." European Spine Journal 17, no. 8 (August 2008): 1006–11. http://dx.doi.org/10.1007/s00586-008-0689-7.

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48

Nishimura, Tsunehiko, Masaharu Sada, Hidemiki Sasaki, Chikao Yutani, Takahiro Kozuka, Hiroshi Amemiya, Tsuyoshi Fujita, Tetsuzo Akutsu, and Hisao Manabe. "Identification of cardiac rejection with magnetic resonance imaging in heterotopic heart transplantation model." Heart and Vessels 3, no. 3 (September 1987): 135–40. http://dx.doi.org/10.1007/bf02058789.

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49

Artem’ev, B. A., A. A. Gnedov, and V. Ya Lavrov. "A mathematical model of the identification of the magnetic field of a flowmeter." Measurement Techniques 49, no. 10 (October 2006): 1037–42. http://dx.doi.org/10.1007/s11018-006-0233-2.

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50

Du, Jieru, Yansong Li, and Jun Liu. "The Magnetic Leakage Inversion Method Based on Singular Value Decomposition of Magnetic Dipole Forward Model." MATEC Web of Conferences 232 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201823202009.

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Анотація:

Ferromagnetic materials are widely used in many fields of national economy. In actual engineering, under the influence of stress or environment, ferromagnetic materials can be defective and have serious consequences. Therefore, magnetic flux leakage inversion, which is speculating defects information according to the detected magnetic leakage signals, is of great practical significance. In allusion to the identification of irregular defects, this paper presented an inversion method based on singular value decomposition of magnetic dipole forward model, which is very effective in identifying irregular defects. This paper contrasted and analyzed the distribution characteristics of magnetic intensity horizontal component Mx when there was no defect and irregular defect, and the comparison verified that the magnetic intensity horizontal component Mx could be used as an inversion gist. Then this paper presented the magnetic dipole forward model B=LM. On account of the magnetic intensity component M containing defects information, this paper adopted the arithmetic of singular value decomposition of coefficient matrix L to solve the inversion equation LM=B and then acquired the distribution of magnetic intensity component M. In the end, this paper verified the validity of this method.

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