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Статті в журналах з теми "Rotating electrical machines"

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Автор: Grafiati

Опубліковано: 1 червня 2024

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1

Hernández-Millán, Rafael, and Jesús Rafael Pacheco-Pimentel. "Recycling rotating electrical machines." Revista Facultad de Ingeniería Universidad de Antioquia, no. 83 (June 2017): 56. http://dx.doi.org/10.17533/udea.redin.n83a07.

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2

Williamson, A. C. "Superconducting Rotating Electrical Machines." IEE Proceedings B Electric Power Applications 132, no. 5 (1985): 298. http://dx.doi.org/10.1049/ip-b.1985.0044.

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3

Ishizaki, Akira, and Yuugi Akiyama. "Advance and Diversification of Rotating Electrical Machines. Diversification of Rotating Electrical Machines." IEEJ Transactions on Industry Applications 115, no. 7 (1995): 838–42. http://dx.doi.org/10.1541/ieejias.115.838.

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4

Ismagilov, Flur, Vyacheslav Vavilov, Valentina Ayguzina, and Vladimir Bekuzin. "New Method of Optimal Design of Electrical Rotating Machines." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 479. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp479-487.

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The article presents a new method of the optimal design of the electrical rotating machine based on genetic algorithm. The mathematical description of the proposed algorithm is developed, and the optimal design of the high-speed electrical rotating machine by proposed method is performed. A new method for optimal design allows obtaining a new electrical rotating machine which mass is lower than mass of the initial electrical rotating machine by two times; the value of the rotor active length is lower by 2.37 times and the current density is higher by 1.7 times in comparison with the initial electrical rotating machine. The losses are increased by only 25 percent (power, rotation frequency and materials of both electrical rotating machine are the same).

5

Schwarz, K. K. "Conference report. Electrical rotating machines." Power Engineering Journal 6, no. 4 (1992): 168. http://dx.doi.org/10.1049/pe:19920034.

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6

Donaghy-Spargo, C. "Rotating electrical machines: Poynting flow." European Journal of Physics 38, no. 5 (August 15, 2017): 055204. http://dx.doi.org/10.1088/1361-6404/aa7dcc.

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7

Belahcen, A. "Magnetoelastic coupling in rotating electrical machines." IEEE Transactions on Magnetics 41, no. 5 (May 2005): 1624–27. http://dx.doi.org/10.1109/tmag.2005.846123.

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8

Huelsmun, L. P. "Visual study of rotating electrical machines." IEEE Circuits and Devices Magazine 18, no. 4 (July 2002): 3–4. http://dx.doi.org/10.1109/mcd.2002.1021117.

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9

Frosini, Lucia. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review." Energies 13, no. 19 (September 27, 2020): 5066. http://dx.doi.org/10.3390/en13195066.

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This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.

10

Khan, Muhammad Amir, Bilal Asad, Karolina Kudelina, Toomas Vaimann, and Ants Kallaste. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art." Energies 16, no. 1 (December 27, 2022): 296. http://dx.doi.org/10.3390/en16010296.

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Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.

11

Wilder, Aleta, and R. Neves. "Fillers in insulation for rotating electrical machines." IEEE Transactions on Dielectrics and Electrical Insulation 17, no. 5 (October 2010): 1357–63. http://dx.doi.org/10.1109/tdei.2010.5595536.

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12

Caramitu, Alina Ruxandra, Marius Bumbac, Cristina Mihaela Nicolescu, and Traian Zaharescu. "Alkyd hybrid coatings for electrical rotating machines." Journal of Thermal Analysis and Calorimetry 134, no. 3 (August 27, 2018): 2017–27. http://dx.doi.org/10.1007/s10973-018-7638-4.

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13

Howey, David A., Peter R. N. Childs, and Andrew S. Holmes. "Air-Gap Convection in Rotating Electrical Machines." IEEE Transactions on Industrial Electronics 59, no. 3 (March 2012): 1367–75. http://dx.doi.org/10.1109/tie.2010.2100337.

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14

Haas, Stefan, Mathias Mair, and Katrin Ellermann. "Magneto-Structural analysis of rotating electrical machines." PAMM 16, no. 1 (October 2016): 445–46. http://dx.doi.org/10.1002/pamm.201610211.

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15

Dineva, Adrienn, Amir Mosavi, Sina Faizollahzadeh Ardabili, Istvan Vajda, Shahaboddin Shamshirband, Timon Rabczuk, and Kwok-Wing Chau. "Review of Soft Computing Models in Design and Control of Rotating Electrical Machines." Energies 12, no. 6 (March 18, 2019): 1049. http://dx.doi.org/10.3390/en12061049.

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Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

16

Kumar, M. Sarath, and B. S. Prabhu. "Rotating Machinery Predictive Maintenance Through Expert System." International Journal of Rotating Machinery 6, no. 5 (2000): 363–73. http://dx.doi.org/10.1155/s1023621x00000348.

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Modern rotating machines such as turbomachines, either produce or absorb huge amount of power. Some of the common applications are: steam turbine-generator and gas turbine-compressor-generator trains produce power and machines, such as pumps, centrifugal compressors, motors, generators, machine tool spindles, etc., are being used in industrial applications. Condition-based maintenance of rotating machinery is a common practice where the machine's condition is monitored constantly, so that timely maintenance can be done. Since modern machines are complex and the amount of data to be interpreted is huge, we need precise and fast methods in order to arrive at the best recommendations to prevent catastrophic failure and to prolong the life of the equipment. In the present work using vibration characteristics of a rotor-bearing system, the condition of a rotating machinery (electrical rotor) is predicted using an off-line expert system. The analysis of the problem is carried out in an Object Oriented Programming (OOP) framework using the finite element method. The expert system which is also developed in an OOP paradigm gives the type of the malfunctions, suggestions and recommendations. The system is implemented in C++.

17

Hraniak, Valerii, and Olena Solona. "PROSPECTS FOR DETECTING DEFECTS IN ROTATING ELECTRIC MACHINES ON THE BASIS OF THE ANALYSIS OF THEIR VIBRATION SIGNALS." Vibrations in engineering and technology, no. 1(104) (April 29, 2022): 20–29. http://dx.doi.org/10.37128/2306-8744-2022-1-3.

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Ensuring the reliability of the operation of technological lines and mechanisms is one of the main conditions for the safe and productive operation of technological equipment. And since one of the key elements of the vast majority of modern equipment and mechanisms are rotating electrical machines, the reliability of the latter largely determines the reliability of the system as a whole. The paper provides a detailed analysis of the prospects of using vibration signals to detect defects in rotating electrical machines. As a result of a statistical study conducted on the basis of studying the causes of emergency failures that occur during the operation of medium-power asynchronous electric motors based on the DTG "Energomash" and the analysis of literary sources, the most probable defects that occur during the operation of rotating electrical machines were identified. In particular, it was found that the most probable defects that occur during the operation of such equipment can be attributed to: rotor imbalance, bearing damage, asymmetry of the stator electromagnetic field and violation of the mechanical rigidity of the supporting structures. Based on the study, it was theoretically substantiated and experimentally confirmed that the most common types of defects that are typical for rotating electrical machines cause a significant vibration response. In this case, the vibration response, containing information about the existing defects of electrical machines, can be measured directly in the operating mode of the electrical machine without the need to intervene in the design of the latter. It was also shown that defects of various types that occur during the operation of rotating electrical machines are characterized by a relatively low severity in the early stages of defect development and selectivity, both in shape and in the frequency spectrum. Therefore, it is obvious that it is necessary to develop highly informative signs of the presence of defects of the considered type, characterized by increased severity and selectivity.

18

Kumar Reddy, Chittimilla Shravan, Muthiah Ramaswamy, and Kartigeyan Jayaraman. "Investigative study on the properties of magnetic materials for electrical machines." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (October 1, 2023): 71. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp71-79.

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<p><span>The paper attempts to investigate the properties of the magnetic materials used in the construction of rotating electric machines, with a view to arrive at the appropriate choice for the induction motors in particular. Measurement of magnetic properties for rotating electrical machines are more important to both machine designer and operator. This paper discusses the influence of magnetic properties of electrical steel on performance of electrical machine. Here properties like BH curve, core loss, permeability and other vital parameters are measured using 400×400 Epstein tester over wide range flux density and frequency up to 50 Hz and 400 Hz. The results measured in terms of the core loss, BH curve and permeability showcase the benefits to decide on the suitability of the material for use in practice.</span></p>

19

Żyluk, Andrzej, Mariusz Zieja, Justyna Tomaszewska, Mariusz Michalski, and Krzysztof Kordys. "Service Life Prediction for Rotating Electrical Machines on Aircraft in Terms of Temperature Loads." Energies 16, no. 1 (December 25, 2022): 218. http://dx.doi.org/10.3390/en16010218.

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This article is focused on the research concerning the calculation of the durability of electrical machines installed in the electrical system of M-28 and C-130 Hercules aircraft. The article presents a method of predicting the service life of aircraft commutator machines, which are the primary source of electrical energy. The result of the research was the determination of the durability of bearings and coils of electrical machines operated on aircraft based on a flight profile analysis. It is problematic to directly measure the wear of bearings and windings of an electric machine on the aircraft. Their usage can be determined from the relation between their wear and the ambient temperature. This research can be used in practice to plan maintenance work on the analyzed aircrafts.

20

Thul, Andreas, Simon Steentjes, Benedikt Schauerte, Piotr Klimczyk, Patrick Denke, and Kay Hameyer. "Rotating magnetizations in electrical machines: Measurements and modeling." AIP Advances 8, no. 5 (May 2018): 056815. http://dx.doi.org/10.1063/1.5007751.

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21

Belahcen, Anouar, Katarzyna Fonteyn, Reijo Kouhia, Paavo Rasilo, and Antero Arkkio. "Magnetomechanical coupled FE simulations of rotating electrical machines." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 32, no. 5 (September 9, 2013): 1484–99. http://dx.doi.org/10.1108/compel-04-2013-0109.

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22

Tavner, P. J. "Review of condition monitoring of rotating electrical machines." IET Electric Power Applications 2, no. 4 (2008): 215. http://dx.doi.org/10.1049/iet-epa:20070280.

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23

Chow, Calvin C. T., Mark D. Ainslie, and K. T. Chau. "High temperature superconducting rotating electrical machines: An overview." Energy Reports 9 (December 2023): 1124–56. http://dx.doi.org/10.1016/j.egyr.2022.11.173.

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24

Sule, Aliyu Hamza. "Rotating Electrical Machines: Types, Applications and Recent Advances." European Journal of Theoretical and Applied Sciences 1, no. 5 (September 1, 2023): 589–97. http://dx.doi.org/10.59324/ejtas.2023.1(5).47.

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The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM.

25

McDermid, Bill. "Insulation of rotating machines [Editorial." IEEE Transactions on Dielectrics and Electrical Insulation 17, no. 5 (October 2010): 1347–48. http://dx.doi.org/10.1109/tdei.2010.5595534.

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26

Chubraeva, L. I., and S. S. Timofeyev. "Conversion of DC Armature Winding into Multi-Phase AC Winding." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012147. http://dx.doi.org/10.1088/1742-6596/2096/1/012147.

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Abstract The paper is dedicated to the principles of transformation of a rotating armature of DC machine into a rotating AC armature of a reverse-type AC alternator, which represents finally the major part of a model brushless exciter. The methodology of this conversion is based on main principles of the theory of electrical machines [1, 2].

27

HRANIAK, Valerii, and Oleh HRYSHCHUK. "DEVELOPMENT OF THE CONCEPT OF BUILDING DIAGNOSTIC SYSTEMS OF ROTATING ELECTRICAL MACHINES UNDER THE CONDITIONS OF LIMITED INFORMATIONALITY OF DIAGNOSTIC SIGNS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 70–77. http://dx.doi.org/10.31891/2307-5732-2022-311-4-70-77.

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The article examines the peculiarities of the construction of systems for diagnosing rotating electric machines in the real conditions of their operation. It is shown that in the specified modes of operation there is a problem of limited informativeness of input information parameters that can be used to build such systems. At the same time, an additional limiting factor that must be considered when designing and implementing such equipment is the limited possibility of intervention in the design of the electric machine, which is usually limited to the manufacturing plant. As a result of a thorough analysis of the latest research in the direction of the development of diagnostic systems for rotating electric machines, a systematization of the technological parameters of electric machines that are most suitable for use in diagnostic systems was carried out. It is shown that when choosing input parameters of diagnostic systems, it is advisable to consider their informativeness, selectivity, expressiveness and complexity of the acquisition algorithm. At the same time, it is substantiated that the choice of the optimal combination of diagnostic features cannot be considered from the point of view of superposition, since each of them will be characterized by the entropy of selectivity and severity relative to defects of different types. The expediency of choosing the type of input information of diagnostic systems based on the method of evolutionary search is shown. It is demonstrated that the mentioned method allows to more completely cover the search space than, for example, gradient optimization methods, and to obtain a solution close to the optimal one in a relatively short time (a small number of iterations). The concept and typical structural diagram of the system for diagnosing rotating electric machines based on a modified non-standard artificial neural network (ANN) and the structure of the ANN itself, which considers the current mode of operation of the electric machine during diagnosis and is characterized by high adaptability to the object of diagnosis, are proposed. An example of its hardware implementation is given.

28

Yoshida, H., and Y. Inoue. "Test Methods of Rotating Machines." IEEE Transactions on Electrical Insulation EI-21, no. 6 (December 1986): 1069–71. http://dx.doi.org/10.1109/tei.1986.349027.

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29

Clenet, S., T. Henneron, and J. Korecki. "Sensor Placement for Field Reconstruction in Rotating Electrical Machines." IEEE Transactions on Magnetics 57, no. 6 (June 2021): 1–4. http://dx.doi.org/10.1109/tmag.2021.3072095.

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30

Pippuri, Jenni, and Antero Arkkio. "2D - 1D time-harmonic model for rotating electrical machines." Pollack Periodica 1, no. 3 (December 2006): 79–90. http://dx.doi.org/10.1556/pollack.1.2006.3.6.

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31

Ebrahimi, Amir. "A Contribution to the Theory of Rotating Electrical Machines." IEEE Access 9 (2021): 113032–39. http://dx.doi.org/10.1109/access.2021.3104013.

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32

Jan Jamali. "End effect in linear induction and rotating electrical machines." IEEE Transactions on Energy Conversion 18, no. 3 (September 2003): 440–47. http://dx.doi.org/10.1109/tec.2003.815853.

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33

Rajagopal, M. S., K. N. Seetharamu, and P. A. Aswatha Narayana. "Finite element analysis of radial cooled rotating electrical machines." International Journal of Numerical Methods for Heat & Fluid Flow 9, no. 1 (February 1999): 18–38. http://dx.doi.org/10.1108/09615539910251086.

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34

Aliyu, Sabo, Maigari Yunana Jeremiah, Kamaluddeen Ibrahim Kanya, Naziru Shuaibu, Aliyu Ahmed, Ahmadu Kenneth, Bukar Alhaji Bukar, et al. "Review on Novel AI-Based Soft Computing Applications in Motor Drives." International Journal of Advanced Natural Sciences and Engineering Researches 7, no. 6 (July 25, 2023): 39–50. http://dx.doi.org/10.59287/ijanser.1134.

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Анотація:

Novel Al-based soft computing applications are vital tools that have greatly upgraded the performance of electrical rotating machines both in design and control. The optimal performance of motors and electric vehicles greatly pends on soft computing techniques advancement in electrical rotating machines. This work presents the view of soft computing techniques and their various applications, most recent of the techniques are reviewed to give a deep inside for further design and control of motor drives.

35

MATVEEV, Aleksey V. "Testing the Hypothesis about the Dependence of the Mass of Electric Machines on the Power and Speed." Elektrichestvo, no. 7 (2021): 4–18. http://dx.doi.org/10.24160/0013-5380-2021-7-4-18.

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The hypothesis stating that the mass of any rotating electric machine is proportional to its power capacity taken to a power of 0.75 and is inversely proportional to the square root of its rotation frequency is tested. The testing is carried out on the material of permanent magnet synchronous machines. The inﬂ uence of such factors as voltage, shape of active parts, cooling, eﬃ ciency level, overloads, presence of supporting structures, power supply frequency, and number of poles is studied. The hypothesis is tested by predicting the mass of machines in the widest possible range of power capacities (from 0.03 W to 36 MW) and rotation frequencies (from 2 to 500 000 rpm). The accuracy of the preliminary estimate of the machine mass within about 50% can be considered acceptable, given that the masses of the machines being estimated range from less than a gram to more than 100 t. Based on the study results, the formulation of a new law of electromechanics is proposed, which describes how the machine mass is predetermined in terms of its power capacity and rotation frequency. The ratio of machine power capacity taken to a power of 0.75 to the square root of its rotation frequency is to some extent similar to the “gravity” which the designers try to overcome working on weakening the eﬀ ect of the above-mentioned inﬂ uencing factors.

36

MATVEEV, Aleksey V. "Testing the Hypothesis about the Dependence of the Mass of Electric Machines on the Power and Speed." Elektrichestvo 7, no. 7 (2021): 4–18. http://dx.doi.org/10.24160//0013-5380-2021-7-4-18.

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The hypothesis stating that the mass of any rotating electric machine is proportional to its power capacity taken to a power of 0.75 and is inversely proportional to the square root of its rotation frequency is tested. The testing is carried out on the material of permanent magnet synchronous machines. The inﬂ uence of such factors as voltage, shape of active parts, cooling, eﬃ ciency level, overloads, presence of supporting structures, power supply frequency, and number of poles is studied. The hypothesis is tested by predicting the mass of machines in the widest possible range of power capacities (from 0.03 W to 36 MW) and rotation frequencies (from 2 to 500 000 rpm). The accuracy of the preliminary estimate of the machine mass within about 50% can be considered acceptable, given that the masses of the machines being estimated range from less than a gram to more than 100 t. Based on the study results, the formulation of a new law of electromechanics is proposed, which describes how the machine mass is predetermined in terms of its power capacity and rotation frequency. The ratio of machine power capacity taken to a power of 0.75 to the square root of its rotation frequency is to some extent similar to the “gravity” which the designers try to overcome working on weakening the eﬀ ect of the above-mentioned inﬂ uencing factors.

37

Thakur, Alka, S. Wadhwani, and A. K. Wadhwani. "BEARING FAULT DETECTION IN ROTATING ELECTRICAL MACHINES USING WAVELET TRANSFORM." Journal of Harmonized Research in Engineering 6, no. 2 (June 30, 2018): 23. http://dx.doi.org/10.30876/johr.6.2.2018.23-27.

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38

Dineva, Adrienn, Amir Mosavi, Mate Gyimesi, Istvan Vajda, Narjes Nabipour, and Timon Rabczuk. "Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification." Applied Sciences 9, no. 23 (November 25, 2019): 5086. http://dx.doi.org/10.3390/app9235086.

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

39

Vajda, I., A. Szalay, N. Gobl, V. Meerovich, and V. Sokolovsky. "Requirements for the industrial application of superconducting rotating electrical machines." IEEE Transactions on Appiled Superconductivity 9, no. 2 (June 1999): 1225–28. http://dx.doi.org/10.1109/77.783521.

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40

Rodger, D., H. C. Lai, R. J. Hill-Cottingham, and P. C. Coles. "Some Tricks for Modeling Rotating Electrical Machines Using Finite Elements." IEEE Transactions on Magnetics 40, no. 2 (March 2004): 802–5. http://dx.doi.org/10.1109/tmag.2004.825320.

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41

Bertotti, G., A. Boglietti, M. Chiampi, D. Chiarabaglio, F. Fiorillo, and M. Lazzari. "An improved estimation of iron losses in rotating electrical machines." IEEE Transactions on Magnetics 27, no. 6 (November 1991): 5007–9. http://dx.doi.org/10.1109/20.278722.

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42

Hutter, W. "Partial discharges. XII. Partial discharge detection in rotating electrical machines." IEEE Electrical Insulation Magazine 8, no. 3 (May 1992): 21–32. http://dx.doi.org/10.1109/57.139068.

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43

Jian Guo Zhu and V. S. Ramsden. "Improved formulations for rotational core losses in rotating electrical machines." IEEE Transactions on Magnetics 34, no. 4 (July 1998): 2234–42. http://dx.doi.org/10.1109/20.703861.

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44

Lima, W. B., and I.-Soella. "The rotating electrical machines diagnosis as an operational guarantee factor." Revue de Métallurgie 102, no. 12 (December 2005): 935–41. http://dx.doi.org/10.1051/metal:2005142.

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45

Caruso, Massimo, Antonino Oscar Di Tommaso, Rosario Miceli, and Renato Rizzo. "The use of slightly asymmetrical windings for rotating electrical machines." International Transactions on Electrical Energy Systems 28, no. 7 (March 14, 2018): e2569. http://dx.doi.org/10.1002/etep.2569.

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46

Hultman, Erik. "Introducing Robotized Stator Cable Winding to Rotating Electric Machines." Machines 10, no. 8 (August 15, 2022): 695. http://dx.doi.org/10.3390/machines10080695.

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Following environmental concerns and the rapid digitalization of our society, we are currently experiencing an extensive electrification and industrial revolution. High numbers of electric machines thus need to be assembled for varying applications, including vehicle propulsion and renewable energy conversion. Cable winding is an alternative stator winding technology for electric machines that has been utilized for such applications, so far in smaller series or in prototype machines. The presented work introduces the first concept for automated stator cable winding of rotating electric machines. This concept could enable higher production volumes of cable wound machines and a unique flexibility in handling different machines, in line with Industry 4.0. Robotized stator cable winding is evaluated here for five very different rotating machine designs, through simulations and analytical extrapolation of previous experimental winding results. Potential cycle time and assembly cost savings are indicated compared to manual and lower volume conventional automation, while it is not possible to compete in the present form with existing very high-volume conventional winding automation for smaller machines. Future experimental work is pointed out on handling larger winding cables and special machine designs, and on increased robustness and optimization.

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Kafeel, Ayaz, Sumair Aziz, Muhammad Awais, Muhammad Attique Khan, Kamran Afaq, Sahar Ahmed Idris, Hammam Alshazly, and Samih M. Mostafa. "An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis." Sensors 21, no. 22 (November 15, 2021): 7587. http://dx.doi.org/10.3390/s21227587.

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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Kumar, Rahul R., Mauro Andriollo, Giansalvo Cirrincione, Maurizio Cirrincione, and Andrea Tortella. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors." Energies 15, no. 23 (November 25, 2022): 8938. http://dx.doi.org/10.3390/en15238938.

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This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

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Miki, Ichiro. "Progress and peripheral technologies of rotating machines." IEEJ Transactions on Electrical and Electronic Engineering 4, no. 1 (January 2009): 46–47. http://dx.doi.org/10.1002/tee.20367.

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Cruz, Jonathan dos Santos, Fabiano Fruett, Renato da Rocha Lopes, Fabio Luiz Takaki, Claudia de Andrade Tambascia, Eduardo Rodrigues de Lima, and Mateus Giesbrecht. "Partial Discharges Monitoring for Electric Machines Diagnosis: A Review." Energies 15, no. 21 (October 27, 2022): 7966. http://dx.doi.org/10.3390/en15217966.

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Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.