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Wyszukujesz frazę "Mohamed, Ahmed" wg kryterium: Autor

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    Tytuł:
    Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach
    Autorzy:
    Birame, M’hamed
    Bessedik, Sid Ahmed
    Benkhoris, Mohamed Fouad
    Powiązania:
    https://bibliotekanauki.pl/articles/1840890.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    fault diagnosis
    partial rupture rotor bar
    spectral analysis
    least square support vector machine
    LS-SVM
    diagnostyka uszkodzeń
    silnik indukcyjny
    wirnik
    analiza widmowa
    maszyna wektorów nośnych
    Opis:
    Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
    Źródło:
    Diagnostyka; 2021, 22, 1; 57-63
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators
    Autorzy:
    Zemali, Zakaria
    Cherroun, Lakhmissi
    Hadroug, Nadji
    Nadour, Mohamed
    Hafaifa, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/2174470.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    fault detection
    estimation
    pitch system
    Kalman filter
    Luenberger observer
    wykrywanie uszkodzeń
    estymacja
    filtr Kalmana
    obserwator Luenbergera
    Opis:
    This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.
    Źródło:
    Diagnostyka; 2023, 24, 1; art. no. 2022110
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent fault diagnosis of power transmission line using fuzzy logic and artificial neural network
    Autorzy:
    Touati, Khaled Omer Mokhtar
    Boudiaf, Mohamed
    Merzouk, Imad
    Hafaifa, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/2146743.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    power system diagnosis
    fault detection
    electrical transmission lines
    ANN
    fuzzy logic
    system elektroenergetyczny
    diagnozowanie
    linia elektroenergetyczna
    wykrywanie uszkodzeń
    logika rozmyta
    sztuczne sieci neuronowe
    Opis:
    In the industrial sector, transmission lines are an important part of the electrical grid. Thus it is important to protect it from all the different faults that may occur as soon as possible to supply the electric power continuously. This paper presents a modern solutions and a comparative study of fault detection and identification in electrical transmission lines using artificial neural network (ANN) compare to the fuzzy logic. Faults in transmission line of various types have been created using simulation model. An intelligent monitoring system (IFD: Intelligent Fault Diagnosis) was used at both ends of a 230 kV overhead transmission line, voltage and current measurements exploited as indicator data for this system. Both approaches were found to be robust, accurate and reliable to detect the fault when it occurs, to determine the fault type short circuit or opening of a power line (open circuit), to locate the fault and to determine which phase was faulted.
    Źródło:
    Diagnostyka; 2022, 23, 4; art. no. 2022410
    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:
    Fault detection and diagnosis of photovoltaic system based on neural networks approach
    Autorzy:
    Rahmoune, Ben Mohamed
    Iratni, Abdelhamid
    Amari, Amel Sabrine
    Hafaifa, Ahmed
    Colak, Ilhami
    Powiązania:
    https://bibliotekanauki.pl/articles/2203647.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    photovoltaic system
    fault detection
    neural networks
    diagnostic system
    residue evaluation
    system fotowoltaiczny
    wykrywanie uszkodzeń
    sieci neuronowe
    system diagnostyczny
    Opis:
    Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.
    Źródło:
    Diagnostyka; 2023, 24, 3; art. no. 2023303
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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