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Wyszukujesz frazę "Wolkiewicz, M." wg kryterium: Autor

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Diagnostic system for induction motor stator winding faults based on axial flux
    Autorzy:
    Wolkiewicz, M.
    Skowron, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193462.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    stator winding fault
    inter-turn short circuit
    axial flux
    diagnostic system
    Opis:
    The article presents the results of research on the use of an axial flux in the diagnostics of induction motor stator winding fed by a frequency converter. Voltage signal waveforms proportional to the axial flux were recorded during motor operation under various conditions and were analyzed with regard to the detection of stator winding short circuits. The taps of the selected coil turns of stator phases were introduced into the tested motor, which allowed to physical modelling inter-turn short circuits. The structure and operation of the computer system used to monitor the state of induction motor windings were discussed. The developed diagnostic system was made in the National Instruments LabVIEW environment. The analysis of faults of the axial flux was made in the detection of induction motor stator winding. The results of experimental research conducted using the developed diagnostic system have also been presented.
    Źródło:
    Power Electronics and Drives; 2017, 2, 37/2; 137-150
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stator winding fault diagnosis of induction motor operating under the field-oriented control with convolutional neural networks
    Autorzy:
    Skowron, M.
    Wolkiewicz, M.
    Tarchała, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/200241.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    diagnostics
    stator faults
    field-oriented control
    convolutional neural networks
    Opis:
    In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1039-1048
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stator windings condition diagnosis of voltage inverter-fed induction motor in open and closed-loop control structures
    Autorzy:
    Wolkiewicz, M.
    Tarchała, G.
    Kowalski, C. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/141208.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    induction motor
    shorted turns
    scalar control
    vector control DFOC
    Opis:
    This paper deals with detection of the stator windings shorted turns in an induction motor drive working under open (scalar) and closed loop (Direct Field Oriented DFO) control structures. In order to detect the early stage of stator winding fault, the analysis of symmetrical and principal components of stator voltages and currents is used. Experimental results obtained from a specially prepared induction motor are presented.
    Źródło:
    Archives of Electrical Engineering; 2015, 64, 1; 67-79
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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