Journal articles on the topic 'Multiple three-phase electrical machines'
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Zabaleta, Mikel, Emil Levi, and Martin Jones. "A Novel Synthetic Loading Method for Multiple Three-Phase Winding Electric Machines." IEEE Transactions on Energy Conversion 34, no. 1 (March 2019): 70–78. http://dx.doi.org/10.1109/tec.2018.2850976.
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Abduallah, Ahmad Anad, Obrad Dordevic, Martin Jones, and Emil Levi. "Regenerative Test for Multiple Three-Phase Machines With Even Number of Neutral Points." IEEE Transactions on Industrial Electronics 67, no. 3 (March 2020): 1684–94. http://dx.doi.org/10.1109/tie.2019.2903750.
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Shchur, Ihor, and Daniel Jancarczyk. "Electromagnetic Torque Ripple in Multiple Three-Phase Brushless DC Motors for Electric Vehicles." Electronics 10, no. 24 (December 13, 2021): 3097. http://dx.doi.org/10.3390/electronics10243097.
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This paper investigated an electromagnetic torque ripple level of BLDC drives with multiple three-phase (TP) permanent magnet (PM) motors for electric vehicles. For this purpose, mathematical models of PM machines of different armature winding sets-single (STP), dual (DTP), triple (TTP), and quadruple (QTP) ones of asymmetrical configuration and optimal angular displacement between winding sets were developed and corresponding computer models in the Matlab/Simulink environment were created. In conducted simulation, the influence of various factors on the electromagnetic torque ripple of the multiple-TP BLDC drives was investigated—degree of modularity, magnetic coupling between armature winding sets, and drive operation in open and closed-loop control systems. Studies have shown an increase of the electromagnetic torque ripple generated by one module in the multiple TP BLDC drives with magnetically coupled winding sets, due to additional current pulsations caused by magnetic interactions between the machine modules. However, the total electromagnetic torque ripples are much lower than in similar drives with magnetically insulated winding sets. Compared with the STP BLDC drive, the multiple TP BLDC drives with the same output parameters showed a reduction of the electromagnetic torque ripple by 27.6% for the DTP, 32.3% for the TTP, and 34.0% for the QTP BLDC drive.
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Cistelecan, Mihail V., Fernando J. T. E. Ferreira, and Mihail Popescu. "Adjustable Flux Three-Phase AC Machines With Combined Multiple-Step Star-Delta Winding Connections." IEEE Transactions on Energy Conversion 25, no. 2 (June 2010): 348–55. http://dx.doi.org/10.1109/tec.2009.2035692.
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Jedryczka, Cezary. "Comparative analysis of the three- and six-phase fractional slot concentrated winding permanent magnet machines." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 3 (May 2, 2017): 811–23. http://dx.doi.org/10.1108/compel-09-2016-0431.
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Purpose The purpose of this paper is to analyse and compare the functional parameters of three- and six-phase permanent magnet synchronous motors (PMSM) with fractional-slot concentrated windings (FSCW). Design/methodology/approach The investigations are focused on the comparison of the distortions of back electromotive force (emf) and magnetomotive force (mmf) waveforms, as well as torque ripples, radial force spatial harmonics and motor performance studies. The finite element models of the test machine and a personally developed computer code have been used to calculate motor characteristics and analyse and synthesise multiphase winding layouts, respectively. Findings Compared with the traditional three-phase PMSM designs, the proposed six-phase machines are characterized by a significantly lower content of sub-harmonics in mmf waveform distribution. Moreover, the investigated six-phase machines exhibited a higher average value of electromagnetic torque, significantly lower torque ripples and a reduced value of low-order harmonics of the radial component of the electromagnetic force in the air-gap of the machine. Originality/value The analyses presented in this paper show that six-phase PMSM with FSCWs are advantageous to their counterpart three-phase machines. Specifically, they are more suited to working with multiple drives supplying a segmented winding system while simultaneously offering higher performance. This suitability to the use of a multi-drive supply for one motor offers flexibility and cost reduction while increasing the fault tolerance of a power train system.
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Jacobina, Cursino BrandÃo, Euzeli Cipriano dos Santos, Edison Roberto Cabral da Silva, Mauricio BeltrÃo de Rossiter Correa, Antonio Marcus Nogueira Lima, and Talvanes Meneses Oliveira. "Reduced Switch Count Multiple Three-Phase AC Machine Drive Systems." IEEE Transactions on Power Electronics 23, no. 2 (March 2008): 966–76. http://dx.doi.org/10.1109/tpel.2007.915027.
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Du, Changshen, Quanbao Cheng, Kai Li, and Yong Yu. "Self-Sustained Collective Motion of Two Joint Liquid Crystal Elastomer Spring Oscillator Powered by Steady Illumination." Micromachines 13, no. 2 (February 8, 2022): 271. http://dx.doi.org/10.3390/mi13020271.
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For complex micro-active machines or micro-robotics, it is crucial to clarify the coupling and collective motion of their multiple self-oscillators. In this article, we construct two joint liquid crystal elastomer (LCE) spring oscillators connected by a spring and theoretically investigate their collective motion based on a well-established dynamic LCE model. The numerical calculations show that the coupled system has three steady synchronization modes: in-phase mode, anti-phase mode, and non-phase-locked mode, and the in-phase mode is more easily achieved than the anti-phase mode and the non-phase-locked mode. Meanwhile, the self-excited oscillation mechanism is elucidated by the competition between network that is achieved by the driving force and the damping dissipation. Furthermore, the phase diagram of three steady synchronization modes under different coupling stiffness and different initial states is given. The effects of several key physical quantities on the amplitude and frequency of the three synchronization modes are studied in detail, and the equivalent systems of in-phase mode and anti-phase mode are proposed. The study of the coupled LCE spring oscillators will deepen people’s understanding of collective motion and has potential applications in the fields of micro-active machines and micro-robots with multiple coupled self-oscillators.
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Jedryczka, Cezary, Wojciech Szelag, and Zbigniew Jerry Piech. "Multiphase permanent magnet synchronous motors with fractional slot windings." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 6 (November 7, 2016): 1937–48. http://dx.doi.org/10.1108/compel-03-2016-0120.
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Purpose The purpose of this paper is to investigate advantages of multiphase permanent magnet synchronous motors (PMSM) with fractional slot concentrated windings (FSCW). The investigation is based on comparative analysis and assessment of FSCW PMSM wound as 3, 6, 9 and 12 phase machines suited for low speed applications. Design/methodology/approach The investigations are focussed on distortions of back electromotive (emf) and magnetomotive force (mmf) with the torque ripples and motors’ performance taken into account. The finite element models with the aid of customized computer code have been adopted for motor winding design and back emf, mmf and motor performance analyses. Findings The novel multiphase winding layouts were found to offer lower content of sub-harmonics in the mmf waveforms compared with the traditional three-phase machine designs. Moreover, the investigated multiphase machines exhibited higher average value of the electromagnetic torque, while the multiphase PMSM machines with FSCW were further characterized by significantly lower torque pulsations. Originality/value The analyses presented in this paper demonstrate that PMSM with FSCW are advantageous to their counterpart three-phase machines. Specifically, they offer higher performance and are more suitable to work with multiple drives supplying segmented winding system. This ability of using multi-drive supply for one motor offers flexibility and cost reduction while increasing fault tolerant power train system.
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Jenal, Mahyuzie, Erwan Sulaiman, Faisal Khan, and Md Zarafi Ahmad. "2D-FEA Based Design Study of Salient Rotor Three-Phase Permanent Magnet Flux Switching Machine with Concentrated Winding." Applied Mechanics and Materials 785 (August 2015): 274–79. http://dx.doi.org/10.4028/www.scientific.net/amm.785.274.
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This paper presents a new structure of permanent magnet flux switching machine (PMFSM) with multiple different sizes of rotor pole width. A robust single piece salient rotor is used to modulate and switch the flux linkage polarity in the armature winding and become the fundamental mechanism of these types of machines. The methodology of two-dimensional (2-D) finite element analysis (FEA) is used to evaluate the electromagnetic performance of coil test including flux line distributions, three phase flux linkage, cogging torque as well as induced emf. The resulting performances are analysed based on the variety of rotor pole width to meet the requirement of direct drive propulsion of Electric Vehicles (EVs).
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Chen, Xiao, Jiabin Wang, and Vipulkumar I. Patel. "A Generic Approach to Reduction of Magnetomotive Force Harmonics in Permanent-Magnet Machines With Concentrated Multiple Three-Phase Windings." IEEE Transactions on Magnetics 50, no. 11 (November 2014): 1–4. http://dx.doi.org/10.1109/tmag.2014.2320446.
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Adam, Edriss Eisa Babikir, and A. Sathesh. "Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles." September 2021 3, no. 3 (November 25, 2021): 182–95. http://dx.doi.org/10.36548/jsws.2021.3.005.
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With modernization and technology enhancements on a global scale, environmental consciousness has also been increasing in recent days. Various technologies and automobile industries are vandalized with sustainable solutions and green technologies. Transportation via roadways is mostly preferred for distant travel as well, despite the advancements in airways and railways, due to less capital outlay, door to door service possibility in rural areas etc. The conventional fuel vehicles are a huge contributor to environmental pollution. Electric vehicles are an optimal solution to this issue. The lives of the common masses are not impacted largely by the electric vehicles despite their market commercialization since a few decades. It is due to certain challenges associated with the electrical vehicles. A 100% efficient perpetual machine does not exist yet. Predominantly, challenges related to charging, hinders the success of e-vehicles. Frequent charging is required in case of long-distance travel and other scenarios in the existing vehicles. Based on the respective governments, extensive changes are made in the infrastructure to overcome the issues at the charging stations. In this paper, an enhanced wireless charging module for electric vehicles is presented. The use of multiple coils is emphasized for building up energy and transmitting it. The inductive power transfer mechanism and efficiency of the system are improved with the design of a three-phase coil. The mechanism for assessment of the energy consumed in e-vehicles is also discussed.
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Tiitta, Markku, Valtteri Tiitta, Jorma Heikkinen, Reijo Lappalainen, and Laura Tomppo. "Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning." Sensors 20, no. 4 (February 17, 2020): 1076. http://dx.doi.org/10.3390/s20041076.
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Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.
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Zhao, Yun, Fan Ye, Sheng Li, Ai-Zhong Wang, and Jiang-Qiao Ding. "W-Band Beam-Tilted H-Plane Horn Array Antenna with Wideband Integrated Waveguide Feed Network Based on MMPTE." Micromachines 14, no. 2 (January 19, 2023): 259. http://dx.doi.org/10.3390/mi14020259.
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A W-band H-plane horn array antenna with tilted radiation beam based on waveguide structure is proposed in this paper. The designed antenna array consists of four H-plane antenna elements and a broadband feed network. The distribution of excitations is determined by the theory of maximum power transmission efficiency (MMPTE). A multiple branch coupler, two T-junctions and three fixed phase shifters are employed to construct the feed network, which can generate the required amplitude and phase in broadband frequency range from 80 GHz to 100 GHz. The computer numerical control (CNC) milling machines technology is employed to machine the feed network and antenna. All measured and simulated results are in good agreement, which verify the feasibility of the theory of MMPTE to generate a radiation beam directed to any angle from −35° to 35° with suitable excitation provided by the proposed feed network in this paper.
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Huang, Yi, and Clemens Gühmann. "Temperature estimation of induction machines based on wireless sensor networks." Journal of Sensors and Sensor Systems 7, no. 1 (April 16, 2018): 267–80. http://dx.doi.org/10.5194/jsss-7-267-2018.
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Abstract. In this paper, a fourth-order Kalman filter (KF) algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms) in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA) is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.
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Wang, Bo, Gaurang Vakil, Ye Liu, Tao Yang, Zhuoran Zhang, and Chris Gerada. "Optimization and Analysis of a High Power Density and Fault Tolerant Starter–Generator for Aircraft Application." Energies 14, no. 1 (December 28, 2020): 113. http://dx.doi.org/10.3390/en14010113.
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Permanent magnet synchronous machines provide many dramatic electromagnetic performances such as high efficiency and high power density, which make them more competitive in aircraft electrification, whereas, designing a permanent magnet starter–generator (PMSG), with given consideration to fault tolerance (FT), is a significant challenge and requires great effort. In this paper, a comprehensive FT PMSG design process is proposed which is applied to power systems of turboprops. Firstly, potential slot/pole combinations were selected based on winding factor, harmonic losses and manufacture issues. Then, pursuing high power density, a multiple objective optimization process was carried out to comprehensively rank performances. To meet a fault tolerance target, electrical, magnetic and thermal isolation topologies were investigated and compared, among which 18 slot/12 pole with dual three-phase was selected as the optimal one, with a power density of 7.9 kW/kg. Finally, a finite element analysis verified the performance in normal and post-fault scenarios. The candidate machine has merits concerning high power density and post-fault performance.
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Hamada, Nozomu. "Separation of Multiple Speech Signals by usingTriangular Microphone Array." ECTI Transactions on Electrical Engineering, Electronics, and Communications 6, no. 1 (January 30, 2008): 15–21. http://dx.doi.org/10.37936/ecti-eec.200861.171743.
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Speech source separation has been an important topic to realize speech-based human-machine interfaces or high quality hand-free communication with machines. For source separation, Independent Component Analysis (ICA) and time-frequency masking are powerful methods as a tool of Blind Source Separation (BSS) of speech mixtures. The latter method is based on the assumption called \W-Disjoint Orthogonality" which implies the cell component sparsity of speech in the time-frequency domain. One of the topics treated in this article is to introduce the time-frequency masking scheme is applied to the equilateral triangular array where the three delay estimates from each microphone pairs are obtained. In addition, it is used to improve histogram-mapping algorithm by integrate and coordinate transformation of three delay estimates. Some experiments in real environment for separating multiple sources are performed to verify the effectiveness.
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Tang, Yongming, Shouguang Sun, Wenfei Yu, and Wei Hua. "Thermal Analysis of Water-Cooling Permanent Magnet Synchronous Machine for Port Traction Electric Vehicle." Electronics 12, no. 3 (February 1, 2023): 734. http://dx.doi.org/10.3390/electronics12030734.
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To further increase the torque/power density of a permanent magnet synchronous machine (PMSM) employed for a port traction electric vehicle, improving the thermal dissipation capacity of the cooling system used in the PMSM has become more and more important. This paper focuses on the thermal analysis of a water-cooling 200 kW PMSM for a port traction electric vehicle. First, the size parameters of the machine and the thermal property parameters of the materials used for each component are given. Based on the heat transfer theory, a fast evaluation method for a transient temperature rise in the water-cooling machine under multiple operating conditions is proposed. A lumped parameter thermal network (LPTN) model is established, and the temperature distributions of the machine at different operating conditions are analyzed. Second, under the same conditions, based on computational fluid dynamics (CFD), a three-dimensional (3D) CFD model is constructed. The influence of different cooling structures on temperature distribution is studied. The validity of the proposed fast evaluation method for a transient temperature rise in water-cooling machines under multiple operating conditions is verified. Finally, the results of the CFD and LPTN calculation are verified by experiments; the maximum temperature deviation of the rated speed/rated power operating condition is 8.5%. This paper provides a reference for the design and analysis of port traction electric vehicle machines.
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Rymarczyk, T., G. Kłosowski, T. Cieplak, and K. Niderla. "The use of dual machine learning in industrial electrical tomography." Journal of Physics: Conference Series 2408, no. 1 (December 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2408/1/012023.
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Abstract Machine learning techniques are playing a key role in tomography. Process tomography, also known as industrial tomography, uses a variety of physical phenomena. Contrary to the commonly used computed tomography in medicine, electrical, ultrasound, radio and even optical tomography are used in industry. In electrical tomography we distinguish between impedance and capacitance tomography. This manuscript presents an algorithmic method to allow accurate measurements of reactors and industrial vessels using electrical impedance tomography. Reactors may contain liquids which undergo phase changes resulting in crystallization or gassing. The tomograph can detect gas crystals or bubbles. The innovative contribution of the authors is the development of an original algorithm that allows the conversion of input measurements to 2D images. First, the algorithm trains multiple single-output neural networks, each of which generates a single image pixel. Secondly, two models were used (support vector machines and artificial neural networks), which were assigned to individual pixels of the image. The image was reconstructed using two methods, not one, so the new method was called dual machine learning (DML). In order to assess the effectiveness of the new approach, both homogeneous methods (SVM and ANN) were compared with the new DML method. The results confirmed the higher effectiveness of the new approach.
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Shao, Lingyun, Wei Hua, Z. Q. Zhu, Wentao Huang, Zhongze Wu, Feng Li, and Ming Cheng. "Investigation on Phase Shift Between Multiple Multiphase Windings in Flux-Switching Permanent Magnet Machines." IEEE Transactions on Industry Applications 53, no. 3 (May 2017): 1958–70. http://dx.doi.org/10.1109/tia.2017.2664718.
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Alalibo, Belema P., Bing Ji, and Wenping Cao. "Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology." Energies 15, no. 2 (January 14, 2022): 577. http://dx.doi.org/10.3390/en15020577.
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Multiple techniques continue to be simultaneously utilized in the condition monitoring and fault detection of electric machines, as there is still no single technique that provides an all-round solution to fault finding in these machines. Having various machine fault-detection techniques is useful in allowing the ability to combine two or more in a manner that will provide a more comprehensive application-dependent condition-monitoring solution; especially, given the increasing role these machines are expected to play in man’s transition to a more sustainable environment, where many more electric machines will be required. This paper presents a novel non-invasive optical fiber using a stray flux technique for the condition monitoring and fault detection of induction machines. A giant magnetostrictive transducer, made of terfenol-D, was bonded onto a fiber Bragg grating, to form a composite FBG-T sensor, which utilizes the machines’ stray flux to determine the internal condition of the machine. Three machine conditions were investigated: healthy, broken rotor, and short circuit inter-turn fault. A tri-axial auto-data-logging flux meter was used to obtain stray magnetic flux measurements, and the numerical results obtained with LabView were analyzed in MATLAB. The optimal positioning and sensitivity of the FBG-T sensor were found to be transverse and 19.3810 pm/μT, respectively. The experimental results showed that the FBG-T sensor accurately distinguished each of the three machine conditions using a different order of magnitude of Bragg wavelength shifts, with the most severe fault reaching wavelength shifts of hundreds of picometres (pm) compared to the healthy and broken rotor conditions, which were in the low-to-mid-hundred and high-hundred picometre (pm) range, respectively. A fast Fourier transform (FFT) analysis, performed on the measured stray flux, revealed that the spectral content of the stray flux affected the magnetostrictive behavior of the magnetic dipoles of the terfenol-D transducer, which translated into strain on the fiber gratings.
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Elvin, Niell, and Alex Elvin. "Freeze–thaw actuator and applications." Journal of Intelligent Material Systems and Structures 29, no. 10 (February 23, 2018): 2267–76. http://dx.doi.org/10.1177/1045389x18758207.
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Significant portions of the earth’s land mass undergo annual freeze–thaw cycles, and although water is abundant and practically a free resource, the possibility of using the water–ice phase transition for smart material applications and actuators for machines has not been studied. This article details some of the characteristics of a freeze–thaw actuator, compares it to other smart material actuators, and presents three experimental demonstrations of its potential for engineering applications. The first application is the conversion of the freeze cycle into electrical energy by coupling the freeze–thaw actuator with a bistable piezoelectric element. The second application demonstrates the ability to store energy mechanically and keep a count of multiple freeze–thaw cycles. This stored energy can then be released after a preset number of freeze–thaw cycles. The third application demonstrates a self-powered mechanism that is capable of moving itself one body length per freeze–thaw cycle.
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Inyang, Udeme Ibanga, Ivan Petrunin, and Ian Jennions. "Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning." Sensors 23, no. 2 (January 15, 2023): 1005. http://dx.doi.org/10.3390/s23021005.
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Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.
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Zhang, Xun, Guanghua Xu, Jiachen Kuang, Lin Suo, Sicong Zhang, and Umair Khalique. "A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions." Sensors 21, no. 17 (August 25, 2021): 5714. http://dx.doi.org/10.3390/s21175714.
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Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.
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Asghar, Furqan, Muhammad Talha, and Sung Ho Kim. "Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor." Journal of Control Science and Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1286318.
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Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.
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Polak, Ladislav, Stanislav Rozum, Martin Slanina, Tomas Bravenec, Tomas Fryza, and Aggelos Pikrakis. "Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning." Sensors 21, no. 13 (July 5, 2021): 4605. http://dx.doi.org/10.3390/s21134605.
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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.
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Montero, Eduardo Rodriguez, Markus Vogelsberger, and Thomas Wolbank. "Initial Rotor Position Detection of Induction Machines Using Feedforward Sensorless Saliency Separation." Power Electronics and Drives 6, no. 1 (January 1, 2021): 301–13. http://dx.doi.org/10.2478/pead-2021-0020.
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Abstract The use of induction machine spatial saliencies for sensorless vector control in the proximity of zero electrical frequency has been extensively researched over the last few decades. A robust technique to extract machine saliencies is called voltage step excitation, and it computes a saliency phasor out of phase current derivatives resulting from specific voltage steps generated by the inverter switching. Within the saliency phasor, all machine saliencies appear superposed. For some machine constructions, multiple saliencies are present, containing information about the spatial, magnetic and geometric state of the machine. Due to its direct relation with the rotor angle and its high accuracy, rotor slotting saliency is often chosen as the sensorless control signal. In order to exclusively access rotor slotting, saliency separation needs to be carried out, with the goal of eliminating all non-control saliencies from the saliency phasor. In this research, feedforward harmonic compensation based on look-up tables (LUTs) is chosen. The idea is to estimate each saliency in relation to amplitude and phase shift once, store such information in a torque-dependent LUT and use it for feedforward compensation. Yet, several saliencies are linked to the rotor position and, thus, the stored phase shift in the LUT is fixed to a defined rotor position at which the saliency estimation was performed. For the feedforward compensation to work during each sensorless start-up, an initial rotor slot detection must be carried out. This paper presents a technique to estimate initial rotor angle based only on the inherent characteristics of the induction machine multi-saliencies and an iterative feedforward compensation process that requires no extra resources and only a few PWM (Pulse Width Modulation) periods to achieve initial slot rotor angle. Experimental results measured at two different test benches prove the high accuracy of the method.
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Li, Baizhou, and Qichang Zhang. "The Combined Internal and Principal Parametric Resonances on Continuum Stator System of Asynchronous Machine." Shock and Vibration 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/835104.
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With the increasing requirement of quiet electrical machines in the civil and defense industry, it is very significant and necessary to predict the vibration and noise characteristics of stator and rotor in the early conceptual phase. Therefore, the combined internal and principal parametric resonances of a stator system excited by radial electromagnetic force are presented in this paper. The stator structure is modeled as a continuum double-shell system which is loaded by a varying distributed electromagnetic load. The nonlinear dynamic equations are derived and solved by the method of multiple scales. The influences of mechanical and electromagnetic parameters on resonance characteristics are illustrated by the frequency-response curves. Furthermore, the Runge-Kutta method is adopted to numerically analyze steady-state response for the further understanding of the resonance characteristics with different parameters.
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Elez, Ante, Josip Študir, and Stjepan Tvorić. "Application of Differential Magnetic Field Measurement (DMFM method) in winding fault detection of AC rotating machines as part of expert monitoring systems." Journal of Energy - Energija 67, no. 3 (June 2, 2022): 3–8. http://dx.doi.org/10.37798/201867367.
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Stator and rotor winding damages in rotating machines are result of electrical, mechanical, and thermal stress. Online magnetic field monitoring via permanently installed measuring coils inside air gap is a well-established methodology which enables winding fault detection. The paper deals with a new method for detection of stator and rotor winding inter-turn short circuits of synchronous machines and slip rings induction machines, as well as rupture of rotor bars and cage ring of induction machines. The method novelty is based on differential measurement of magnetic field by using two serial connected measuring coils. They are installed on the places (stator or rotor teeth) in the machine which have, by absolute value, equal magnetic vector potential. The distance between the measuring coils is n·tp, where tp is a pole pitch, and n = 1, 2, 3, 4,... is a multiple of the pole pitch. Measuring the coil-induced voltage enables us to detect stator and rotor winding faults, which means that measured voltage is approximately zero without fault and increases in the presence of fault. Analysis of the measuring signal allows us to detect and locate fault. With this new method it is possible with high sensitivity to determine winding fault, which enables more reliable fault detection. For example, in comparison with the motor current signature analysis method (the most widely used method for motor faults detection), this new method gives 200 times higher sensitivity to fault occurrence. Also, by using the DMFM method, faults can be detected in the time domain and there is no need for spectral or other complex signal analysis. This is very important because the measuring equipment used for machine fault detection can be simple and more economically acceptable. The DMFM method enables fault detection for even small machines with small expense in a very effective way. The only downside of the DMFM method is the fact that machine should be disassembled in order to install measuring coils. This problem is solved during the machine overhaul or during the manufacturing of the machine, when sensors can be easily implemented in the machine. For machines with large air gap, measuring coils can be installed without a machine disassembly. For the purpose of the method testing, numerous finite-element (FE) simulations on the 2- and 3-D machine models have been carried out to verify the method. Powerful numerical tools generate realistic results with properly selected starting and boundary conditions. By FEM models, actual machines with embedded measuring coils where created and simulated. The voltage induced inside the measuring coils is calculated for different machine states, load point and with and without a fault (broken rotor bar or inter-turn short circuit). Also, this method was experimentally validated via series of laboratory tests performed on the real electric machines specially designed for fault study (broken rotor bars, broken ring and inter-turn short circuits in a stator and rotor winding). Additionally, this method is applied on more than 20 real machines in industry. Due to the large amount of measured data, in this paper, it will be presented only one measurement performed on an induction motor on which we have detected one broken rotor bar. The thickness of the measuring coil designed in the printed circuit board technique is 0.3 mm. The number of turns is from 3 to 10. This new method and performed FEM calculations together with the experimental measurements improve fault detection portfolio knowledge that can be used in monitoring and diagnostics of rotating machines. Furthermore, this patent-pending method is already implemented in three innovative products placed on market (expert monitoring systems), so this method is fully confirmed in practice.
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Iqbal, Arif, Farhad Ilahi Bakhsh, and Girish Kumar Singh. "Operation and Testing of Indirect Field Oriented Control of Asymmetrical Six-Phase Open-Ended Winding Induction Machine Using Hardware-in-Loop (HIL) Emulator." International Transactions on Electrical Energy Systems 2023 (January 6, 2023): 1–13. http://dx.doi.org/10.1155/2023/4623140.
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This study deals with an asymmetrical six-phase open-ended winding induction machine in closed loop operation using indirect field-oriented control scheme. The closed loop scheme has been developed by using the two axis (d-q) modeling of six-phase induction machine in rotating synchronous reference frame and can be easily extended to any multiphase machine having the stator winding which are the multiple of three. Operation under steady-state is considered to develop the phasor diagram in closed loop operation of machine in the motoring mode. The performance of the asymmetrical six-phase induction machine (opened ended winding) is investigated in the entire four-quadrant operation. The complete machine drive system was developed by using MATLAB/Simulink, which was used to obtain analytical results in different modes. Furthermore, the analytical results in the motoring mode were experimentally validated through real-time simulation by using Typhoon hardware-in-loop (HIL) emulator.
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Araújo, Ramon C. F., Rodrigo M. S. de Oliveira, and Fabrício J. B. Barros. "Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars." Energies 15, no. 1 (January 4, 2022): 326. http://dx.doi.org/10.3390/en15010326.
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In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.
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Zhou, Hui, Dandan Yang, Zhengyi Li, Dao Zhou, Junfeng Gao, and Jinan Guan. "Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles." Sensors 21, no. 9 (April 22, 2021): 2933. http://dx.doi.org/10.3390/s21092933.
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Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.
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Khlifi, Mohamed Arbi, and Habib Rehaoulia. "General modeling of saturated AC machines for industrial drives." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 35, no. 1 (January 4, 2016): 44–63. http://dx.doi.org/10.1108/compel-12-2014-0346.
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Purpose – When magnetic saturation in ac machines is evolved, the theory of main flux saturation in d-q axes remains the best. Because of its simplicity, it is the most used in either motoring or generating mode for synchronous or asynchronous machines. Although, it is considered as a global way of introducing the iron saturation, compared to other methods, today, its fidelity has no contest in predicting complex ac machine operations. For this purpose, the aims of this paper consists of modeling these machines whatever the state-space variables values are taking into account the magnetic saturation. Two unified procedures are proposed. The first one deals with a common approach to establishing a complete and detailed model synthesis in d-q axes. The second also presents a unified approach to introducing magnetic saturation of the iron core in the characteristic equations. The analysis takes the salient pole synchronous machine as a general case of study. Then the approaches are extended to undamped and smooth air gap synchronous machines as well as induction machines. The paper aims to discuss these issues. Design/methodology/approach – The present paper, which is a first part of a work under study dealing with a unified method to derive multiple models of saturated ac machines, is intended to the description of an alternative method and its application for induction and synchronous machines. It mainly consists of the following parts: first, after writing the stator and rotor space vector d-q equations, the number of possible models is immediately discussed. By considering the currents and fluxes as state-space variables, 14 models are obtained for AC induction machine (IM and SM). They are classified into three families, current (three), flux (three) and mixed models (eight). Second, in order to easily introduce the magnetic saturation in the 14 developed models, a method is presented. It consists of just elaborating the model with the winding currents as state variables, then deriving all the other models from it. Third, to emphasize the influence of the presence of magnetic saturation, in each model, each inductance along the d and q axes is written with a fundamental expression which exists with or without saturation and an additional one due purely to saturation. Hence the additional terms can be studied and quantified in an easy way or simply removed when linear case is assumed. Fourth, adopting such strategy to write the different coefficients of the models had led to the definition of common saturation factors. In turn, the definition of common saturation factors had allowed the definition of different groups of models within each family. Fifth, an alternative to evaluate the static and dynamic saturation coefficients is also proposed. It is shown that by proper fitting of the experimental magnetizing curve, all saturation coefficients can be written only in terms of which is simply the magnitudes ratio of the magnetizing flux and current. Sixth, although the theory of the main flux saturation is now admitted, an investigation was carried out on a self-excited induction generator and the build-up of voltage and current phases of a standalone alternator, to prove the equivalence between the all developed models. Findings – The number of models based on the state-space variables choice, of a saturated ac machine, is reviewed. A simple method consisting of elaborating just the winding currents model, with magnetic saturation and deriving all the other models from it, is presented. In this study special interest was particularly focussed on either novel models or existing models cited in the literature but cannot be obtained by other approaches. In all cases, if the differential equations of the machine are formulated in terms of a set of variables other than the winding currents, a noticeable reduction in the size of equations may be obtained and consequently less time computing. The approach seems to be able to derive any possible model whatever the state-space variables and the type of the ac machine and hence can be classified as a general approach. Practical implications – The experiments of synchronous and induction machine transients prove the validity of the method. Originality/value – By suitable choice of state-space characteristic vectors among the fluxes and the currents, a synthesis of AC machine models in d-q axes is established. To introduce magnetic saturation in each model, an approach-based uniquely on the elaboration of the winding currents model is exposed and applied. In addition, the analysis gives a detailed classification of all found models taking into account the state variables nature as well as the cross-coupling coefficient considered as a saturation factor. The study is completed with a simple alternative to evaluate all saturation factors by just calculating the static magnetizing inductance.
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Jaleel, Ahmed Etihad, and Hesham Adnan Alabbasi. "Online hand position detection and classification system using multiple classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (October 1, 2022): 346. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp346-357.
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Hand position recognition is very significant for human-computer interaction. Different kinds of devices and technologies can be used for data acquisition; each has its specification and accuracy, one of these devices is Kinect V2 sensor. A three-dimensional location of the skeleton joints is taken from the Kinect device to create three types of data, the first is joint position raw data, the second is angles between joints, the third is combined of both types. These three types of data are used to train four classifiers, which are support vector machines, random forest, k nearest neighbors, and multilayer perceptron. The experiments are done on the datasets of 30,480 frames from 127 volunteers with saved trained models are used to predict and classify the eight positions of hand in a real-time system. The results show that our proposed approach performs well with highly efficient and accuracy reaching up to 99.07% in some cases and an average time spent on checking frame by frame sequentially very short period, and some cases, it reaches 0.59*10-3 seconds. This system can used in many applications such as controlling robots or devices, comparing physical exercises, or even monitoring elderly and patients, and more.
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Yepes, Alejandro G., Oscar Lopez, Ignacio Gonzalez-Prieto, Mario J. Duran, and Jesus Doval-Gandoy. "A Comprehensive Survey on Fault Tolerance in Multiphase AC Drives, Part 1: General Overview Considering Multiple Fault Types." Machines 10, no. 3 (March 14, 2022): 208. http://dx.doi.org/10.3390/machines10030208.
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Multiphase drives offer enhanced fault-tolerant capabilities compared with conventional three-phase ones. Their phase redundancy makes them able to continue running in the event of faults (e.g., open/short-circuits) in certain phases. Moreover, their greater number of degrees of freedom permits improving diagnosis and performance, not only under faults affecting individual phases, but also under those affecting the machine/drive as a whole. That is the case of failures in the dc link, resolver/encoder, control unit, cooling system, etc. Accordingly, multiphase drives are becoming remarkable contenders for applications where high reliability is required, such as electric vehicles and standalone/off-shore generation. Actually, the literature on the subject has grown exponentially in recent years. Various review papers have been published, but none of them currently cover the state-of-the-art in a comprehensive and up-to-date fashion. This two-part paper presents an overview concerning fault tolerance in multiphase drives. Hundreds of citations are classified and critically discussed. Although the emphasis is put on fault tolerance, fault detection/diagnosis is also considered to some extent, because of its importance in fault-tolerant drives. The most important recent advances, emerging trends and open challenges are also identified. Part 1 provides a comprehensive survey considering numerous kinds of faults, whereas Part 2 is focused on phase/switch open-circuit failures.
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Nagano, Tatsuki, Ryosuke Yajima, Shunsuke Hamasaki, Keiji Nagatani, Alessandro Moro, Hiroyuki Okamoto, Genki Yamauchi, Takeshi Hashimoto, Atsushi Yamashita, and Hajime Asama. "Arbitrary Viewpoint Visualization for Teleoperated Hydraulic Excavators." Journal of Robotics and Mechatronics 32, no. 6 (December 20, 2020): 1233–43. http://dx.doi.org/10.20965/jrm.2020.p1233.
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In this paper, we propose a visualization system for the teleoperation of excavation works using a hydraulic excavator. An arbitrary viewpoint visualization system is a visualization system that enables teleoperators to observe the environment around a machine by combining multiple camera images. However, when applied to machines with arms (such as hydraulic excavators), a part of the field of view is shielded by the image of the excavator’s arm; hence, an occlusion occurs behind the arm. Furthermore, it is difficult for teleoperators to understand the three-dimensional (3D) condition of the excavating point because the current system approximates the surrounding environment with a predetermined shape. To solve these problems, we propose two methods: (1) a method to reduce the occluded region and expand the field of view, and (2) a method to measure and integrate the 3D information of the excavating point to the image. In addition, we conduct experiments using a real hydraulic excavator, and we demonstrate that an image with sufficient accuracy can be presented in real-time.
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Rao, Sivakavi Naga Venkata Bramareswara, Venkata Pavan Kumar Yellapragada, Kottala Padma, Darsy John Pradeep, Challa Pradeep Reddy, Mohammad Amir, and Shady S. Refaat. "Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods." Energies 15, no. 17 (August 23, 2022): 6124. http://dx.doi.org/10.3390/en15176124.
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The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.
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Matsuo, Muneyuki, Yuiko Hirata, Kensuke Kurihara, Taro Toyota, Toru Miura, Kentaro Suzuki, and Tadashi Sugawara. "Environment-Sensitive Intelligent Self-Reproducing Artificial Cell with a Modification-Active Lipo-Deoxyribozyme." Micromachines 11, no. 6 (June 22, 2020): 606. http://dx.doi.org/10.3390/mi11060606.
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As a supramolecular micromachine with information flow, a giant vesicle (GV)-based artificial cell that exhibits a linked proliferation between GV reproduction and internal DNA amplification has been explored in this study. The linked proliferation is controlled by a complex consisting of GV membrane-intruded DNA with acidic amphiphilic catalysts, working overall as a lipo-deoxyribozyme. Here, we investigated how a GV-based artificial cell containing this lipo-deoxyribozyme responds to diverse external and internal environments, changing its proliferative dynamics. We observed morphological changes (phenotypic expression) in GVs induced by the addition of membrane precursors with different intervals of addition (starvation periods). First, we focused on a new phenotype, the “multiple tubulated” form, which emerged after a long starvation period. Compared to other forms, the multiple tubulated form is characterized by a larger membrane surface with a heavily cationic charge. A second consideration is the effect of the chain length of encapsulated DNA on competitive proliferation. The competitive proliferation among three different species of artificial cells containing different lengths of DNA was investigated. The results clearly showed a distinct intervention in the proliferation dynamics of the artificial cells with each other. In this sense, our GV-based artificial cell can be regarded as an intelligent supramolecular machine responding to external and internal environments, providing a new concept for developing molecular machines and robotics.
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Lencse, Gábor, and Youki Kadobayashi. "Methodology for DNS Cache Poisoning Vulnerability Analysis of DNS64 Implementations." Infocommunications journal, no. 2 (2018): 13–25. http://dx.doi.org/10.36244/icj.2018.2.3.
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The trustworthy operation of the DNS service is a very important precondition for a secure Internet. As we point it out, DNS cache poisoning could be even more dangerous if it is performed against DNS64 servers. Based on RCF 5452, we give an introduction to the three main components of DNS cache poisoning vulnerability, namely Transaction ID prediction, source port number prediction, and birthday paradox based attack, which is possible if a DNS or DNS64 server sends out multiple equivalent queries (with identical QNAME, QTYPE, and QCLASS fields) concurrently. We design and implement a methodology and a testbed, which can be used for the systematic testing of DNS or DNS64 implementations, whether they are susceptible to these three vulnerabilities. We perform the tests with the following DNS64 implementations: BIND, PowerDNS, Unbound, TOTD (two versions) and mtd64-ng. As for the testbed, we use three virtual Linux machines executed by a Windows 7 host. As for tools, we use VMware Workstation 12 Player for virtualization, Wireshark and tshark for monitoring, dns64perf for Transaction ID and source port predictability tests, and our currently developed "birthday-test" program for concurrently sent multiple equivalent queries testing. Our methodology can be used for DNS cache poisoning vulnerablility analysis of further DNS or DNS64 implementations. A testbed with the same structure may be used for security vulnerablility analysis of DNS or DNS64 servers and also NAT64 gateways concerning further threats.
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Manjunath, Chinthakunta, Balamurugan Marimuthu, and Bikramaditya Ghosh. "Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 3549. http://dx.doi.org/10.11591/ijece.v13i3.pp3549-3560.
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<span lang="EN-US">Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.</span>
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Lou, Zhi-Feng, Xiu-Peng Hao, Yin-Di Cai, Tien-Fu Lu, Xiao-Dong Wang, and Kuang-Chao Fan. "An Embedded Sensor System for Real-Time Detecting 5-DOF Error Motions of Rotary Stages." Sensors 19, no. 13 (June 27, 2019): 2855. http://dx.doi.org/10.3390/s19132855.
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The geometric error motions of rotary stages greatly affect the accuracy of constructed machines such as machine tools, measuring instruments, and robots. In this paper, an embedded sensor system for real-time measurement of two radial and three angular error motions of a rotary stage is proposed, which makes use of a rotary encoder with multiple scanning heads to measure the rotational angle and two radial error motions and a miniature autocollimator to measure two tilt angular errors of the axis of rotation. The assembly errors of the grid disc of the encoder and the mirror for autocollimator are also evaluated and compensated. The developed measuring device can be fixed inside the rotary stage. In the experiments, radial error motions of two points on the axis (h = 5 mm and 60 mm) were measured and calibrated with LVDTs, and the data showed that the radial error motions of the axis were less than 20 μm, and the calibration residual errors were less than 2 μm. When intermittent external forces were applied to the stage, the change of the stage’s error motion could also be monitored accurately.
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Sundaram, Sarvesh, and Abe Zeid. "Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework." Sensors 21, no. 18 (September 7, 2021): 5994. http://dx.doi.org/10.3390/s21185994.
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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.
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Areiza-Laverde, Henry Jhoán, Andrés Eduardo Castro-Ospina, María Liliana Hernández, and Gloria M. Díaz. "A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling." Sensors 20, no. 14 (July 14, 2020): 3919. http://dx.doi.org/10.3390/s20143919.
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Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.
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Kadri, Farid, and Mohamed A. Hamida. "Neural Direct Torque Control for Induction Motor under Voltage Source Inverter Open Switch Fault." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 4 (July 5, 2020): 571–79. http://dx.doi.org/10.2174/1874476105666190830103616.
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Background: The study of induction motor behavior during abnormal conditions due to the presence of faults and the possibility to diagnose these abnormal conditions has been a challenging topic for many electrical machine researchers. Objective: Direct Torque Control (DTC) is applied to the control of an induction motor in healthy and an open circuit fault in the PWM three phase voltage fed inverter. Neural DTC is developed and used to improve the dynamic behavior of the drive system under faulty switch occurrence. Methods: The validity of the proposed control scheme is tested under normal conditions and switching fail in the Voltage Source Inverter (VSI) caused by an open circuit. Through a simulation testing of an induction motor drive system at different speed references, a comparison between basic DTC and Neural DTC is performed. Results: Simulated results on 1.5-kW induction motor drive show the performance of the proposed control in normal and faulty cases. The stator current, flux, torque, and speed at different references are determined and compared in the above techniques using MATLAB-SIMULINK. Conclusion: A Neural Network (NN) DTC control system under an open switch fault is proposed without the need for classical switching table. The use of hybrid intelligent techniques aims to improve the DTC performances in case of multiple faults occurrence.
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Owusu-Ansah, Ebenezer, and Colin Dalton. "Fabrication of a 3D Multi-Depth Reservoir Micromodel in Borosilicate Glass Using Femtosecond Laser Material Processing." Micromachines 11, no. 12 (December 6, 2020): 1082. http://dx.doi.org/10.3390/mi11121082.
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Micromodels are ideal candidates for microfluidic transport investigations, and they have been used for many applications, including oil recovery and carbon dioxide storage. Conventional fabrication methods (e.g., photolithography and chemical etching) are beset with many issues, such as multiple wet processing steps and isotropic etching profiles, making them unsuitable to fabricate complex, multi-depth features. Here, we report a simpler approach, femtosecond laser material processing (FLMP), to fabricate a 3D reservoir micromodel featuring 4 different depths—35, 70, 140, and 280 µm, over a large surface area (20 mm × 15 mm) in a borosilicate glass substrate. The dependence of etch depth on major processing parameters of FLMP, i.e., average laser fluence (LFav), and computer numerically controlled (CNC) processing speed (PSCNC), was studied. A linear etch depth dependence on LFav was determined while a three-phase exponential decay dependence was obtained for PSCNC. The accuracy of the method was investigated by using the etch depth dependence on PSCNC relation as a model to predict input parameters required to machine the micromodel. This study shows the capability and robustness of FLMP to machine 3D multi-depth features that will be essential for the development, control, and fabrication of complex microfluidic geometries.
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Pham, Tuyen, Dat Nguyen, Chanhum Park, and Kang Park. "Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors." Sensors 19, no. 4 (February 15, 2019): 792. http://dx.doi.org/10.3390/s19040792.
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Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks.
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Martinez-Castillo, Cecilia, Gonzalo Astray, and Juan Carlos Mejuto. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models." Energies 14, no. 8 (April 20, 2021): 2332. http://dx.doi.org/10.3390/en14082332.
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Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.
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Chand, Praneel, and Sunil Lal. "Vision-Based Detection and Classification of Used Electronic Parts." Sensors 22, no. 23 (November 23, 2022): 9079. http://dx.doi.org/10.3390/s22239079.
Full textAbstract:
Economic and environmental sustainability is becoming increasingly important in today’s world. Electronic waste (e-waste) is on the rise and options to reuse parts should be explored. Hence, this paper presents the development of vision-based methods for the detection and classification of used electronics parts. In particular, the problem of classifying commonly used and relatively expensive electronic project parts such as capacitors, potentiometers, and voltage regulator ICs is investigated. A multiple object workspace scenario with an overhead camera is investigated. A customized object detection algorithm determines regions of interest and extracts data for classification. Three classification methods are explored: (a) shallow neural networks (SNNs), (b) support vector machines (SVMs), and (c) deep learning with convolutional neural networks (CNNs). All three methods utilize 30 × 30-pixel grayscale image inputs. Shallow neural networks achieved the lowest overall accuracy of 85.6%. The SVM implementation produced its best results using a cubic kernel and principal component analysis (PCA) with 20 features. An overall accuracy of 95.2% was achieved with this setting. The deep learning CNN model has three convolution layers, two pooling layers, one fully connected layer, softmax, and a classification layer. The convolution layer filter size was set to four and adjusting the number of filters produced little variation in accuracy. An overall accuracy of 98.1% was achieved with the CNN model.
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Noreen, Iram, Muhammad Hamid, Uzma Akram, Saadia Malik, and Muhammad Saleem. "Hand Pose Recognition Using Parallel Multi Stream CNN." Sensors 21, no. 24 (December 18, 2021): 8469. http://dx.doi.org/10.3390/s21248469.
Full textAbstract:
Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.
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Kim, Keonwook, and Yujin Hong. "Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution." Sensors 23, no. 2 (January 9, 2023): 769. http://dx.doi.org/10.3390/s23020769.
Full textAbstract:
To extract the phase information from multiple receivers, the conventional sound source localization system involves substantial complexity in software and hardware. Along with the algorithm complexity, the dedicated communication channel and individual analog-to-digital conversions prevent an increase in the system’s capability due to feasibility. The previous study suggested and verified the single-channel sound source localization system, which aggregates the receivers on the single analog network for the single digital converter. This paper proposes the improved algorithm for the single-channel sound source localization system based on the Gaussian process regression with the novel feature extraction method. The proposed system consists of three computational stages: homomorphic deconvolution, feature extraction, and Gaussian process regression in cascade. The individual stages represent time delay extraction, data arrangement, and machine prediction, respectively. The optimal receiver configuration for the three-receiver structure is derived from the novel similarity matrix analysis based on the time delay pattern diversity. The simulations and experiments present precise predictions with proper model order and ensemble average length. The nonparametric method, with the rational quadratic kernel, shows consistent performance on trained angles. The Steiglitz–McBride model with the exponential kernel delivers the best predictions for trained and untrained angles with low bias and low variance in statistics.
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Zhang, Hengliang, Paolo Giangrande, Giacomo Sala, Zeyuan Xu, Wei Hua, Vincenzo Madonna, David Gerada, and Chris Gerada. "Thermal Model Approach to Multisector Three-Phase Electrical Machines." IEEE Transactions on Industrial Electronics 68, no. 4 (April 2021): 2919–30. http://dx.doi.org/10.1109/tie.2020.2977559.
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