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    Wyświetlanie 1-3 z 3
    Tytuł:
    Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor
    Autorzy:
    Ewert, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1193708.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    induction motor
    stator faults
    rotor faults
    diagnostics
    axial flux
    neural network
    Opis:
    The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB–Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.
    Źródło:
    Power Electronics and Drives; 2019, 4, 39; 201-213
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
    Autorzy:
    Jankowska, Kamila
    Ewert, Pawel
    Powiązania:
    https://bibliotekanauki.pl/articles/1955971.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    PMSM
    rolling bearing
    electric drive diagnostics
    self-organising map
    shallow neural network
    Opis:
    Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.
    Źródło:
    Power Electronics and Drives; 2021, 6, 41; 100-112
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Unraveling Induction Motor State through Thermal Imaging and Edge Processing : A Step towards Explainable Fault Diagnosis
    Autorzy:
    Piechocki, Mateusz
    Pajchrowski, Tomasz
    Kraft, Marek
    Wolkiewicz, Marcin
    Ewert, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/27312790.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    thermal imaging
    fault diagnosis
    squirrel-cage induction motor
    convolutional neural networks
    explainability
    edge processing
    Opis:
    Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 3; art. no. 170114
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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