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    Wyświetlanie 1-4 z 4
    Tytuł:
    Structural diagram of the built-in diagnostic system for electric drives of vehicles
    Autorzy:
    Gubarevych, Oleg
    Goolak, Sergey
    Melkonova, Inna
    Yurchenko, Mariia
    Powiązania:
    https://bibliotekanauki.pl/articles/2146742.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    diagnostic system
    induction electric motor
    inter-turn short circuit
    stator defect
    system diagnostyczny
    silnik elektryczny indukcyjny
    zwarcie międzyzwojowe
    wada stojana
    Opis:
    Currently, in transport systems, as part of the main and auxiliary equipment, a large number of induction motors with a squirrel-cage rotor of different capacities are used. Their wide application in the transport industry is associated with the main advantages over other types of machines – a fairly high reliability, low cost and ease of maintenance. However, during the operation of these motors, a number of malfunctions can occur that affect the deterioration of the performance of the entire drive, the accuracy of its functions, or accelerate an emergency stop. To ensure proper control of the technical condition of electric motors, modern diagnostic systems are required that operate in real-time and operational loading mode with the transmission of data on the instantaneous state of the main control elements. The paper proposes a block diagram of the diagnostic built-in system and developed a modular unit for it to set the type and degree of the most complex damage - inter-turn short circuit in the stator winding.
    Źródło:
    Diagnostyka; 2022, 23, 4; art. no. 2022406
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
    Autorzy:
    Birame, M’hamed
    Taibi, Djamel
    Bessedik, Sid Ahmed
    Benkhoris, Mohamed Fouad
    Powiązania:
    https://bibliotekanauki.pl/articles/327458.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    induction motor
    inter-turn short circuit
    fault diagnosis
    least square support vector machine
    LS-SVM
    silnik indukcyjny
    zwarcie międzyzwojowe
    diagnostyka uszkodzeń
    Opis:
    Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.
    Źródło:
    Diagnostyka; 2020, 21, 4; 35-41
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Early detection and localization of stator inter-turn short circuit faults based on variational mode decomposition and deep learning in induction motor
    Autorzy:
    Guedidi, Asma
    Laala, Widad
    Guettaf, Abderrazak
    Arif, Ali
    Powiązania:
    https://bibliotekanauki.pl/articles/27313828.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    convolutional neural networks
    CNNs
    deep learning
    short circuit fault diagnosis
    variational mode decomposition
    information map
    silnik indukcyjny
    konwolucyjne sieci neuronowe
    uczenie głębokie
    mapa informacyjna
    Opis:
    The existing diagnostic techniques for detecting inter-turn short circuits (ITSCs) in induction motors face two primary challenges. Firstly, they suffer from reduced sensitivity, often failing to detect ITSCs when only a few turns are short-circuited. Secondly, their reliability are compromised by load fluctuations, leading to false alarms even in the absence of actual faults. To address these issues, a novel intelligent approach to diagnose ITSC fault is proposed. Indeed, this method encompasses three core components: a novel multi-sensor fusion technique, a knowledge map, and enhanced Convolutional Neural Networks (CNNs). First, the raw data collected from multiple sensors undergoes a transformation into 2D data using a novel image transformation based on Hilbert transform (HT) and variational mode decomposition (VMD), which is concatenate to a novel information map including frequency fault information and rotational speed. Then, this 3D multi information image is used as input to an improvement CNN model that apply a transfer learning for an enhanced version of SqueezNet with incorporating a novel attention mechanism module to precisely identify fault features. Experimental results and performance comparisons demonstrate that the proposed model attains high performance surpassing other Deep Learning (DL) methods in terms of accuracy. In addition, the model has consistently demonstrated its ability to make precise predictions and accurately classify fault severity, even under different working conditions.
    Źródło:
    Diagnostyka; 2023, 24, 4; art. no. 2023401
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis
    Autorzy:
    Ouamara, Dehbia
    Boukhnifer, Moussa
    Chaibet, Ahmed
    Maidi, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/2174472.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    induction motor
    electrical vehicle
    fault diagnosis
    inter-turn short circuit
    extended Kalman filter
    spectral analysis
    fast Fourier transform
    short-time Fourier transform
    silnik indukcyjny
    pojazd elektryczny
    diagnostyka uszkodzeń
    zwarcie międzyzwojowe
    rozszerzony filtr Kalmana
    analiza spektralna
    szybka transformata Fouriera
    krótkoczasowa transformata Fouriera
    Opis:
    The three-phase induction motor is well suited for a wide range of mobile drives, specifically for electric vehicle powertrain. During the entire life cycle of the electric motor, some types of failures can occur, with stator winding failure being the most common. The impact of this failure must be considered from the incipient as it can affect the performance of the motor, especially for electrically powered vehicle application. In this paper, the intern turn short circuit of the stator winding was studied using Fast Fourier transform (FFT) and Shor-Time Fourier transform (STFT) approaches. The residuals current between the estimated currents provided by the extended Kalman filter (EKF) and the actual ones are used for fault diagnosis and identification. Through FFT, the residual spectrum is sensitive to faults and gives the extraction of inter-turn short circuit (ITSC) related frequencies in the phase winding. In addition, the FFT is used to obtain information about when and where the ITSC appears in the phase winding. Indeed, the results allow to know the faulty phase, to estimate the fault rate and the fault occurrence frequency as well as their appearance time.
    Źródło:
    Diagnostyka; 2023, 24, 1; art. no. 2023113
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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