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    Tytuł:
    Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine
    Autorzy:
    Alili, Bachir
    Hafaifa, Ahmed
    Iratni, Abdelhamid
    Powiązania:
    https://bibliotekanauki.pl/articles/327804.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    faults detection
    fuzzy logic
    decision making
    vibration monitoring
    gas turbines
    turbina gazowa
    logika rozmyta
    wykrywanie uszkodzeń
    podejmowanie decyzji
    monitorowanie drgań
    Opis:
    The use of new technologies in modern industry improves productivity but induces complexity in the industrial system. This complexity makes it vulnerable to faults, which requires significant expense in terms of safety, reliability and availability. Indeed, a diagnostic operation is essential for the operational safety and availability of these industrial systems. This diagnostic operation is based on two important functions which are the detection and localization of anomalies, which consists to verifying the consistency of the data taken in real time from the installation with a reliable model, to ensure the good performance of the monitoring system. Hence, the diagnosis of gas turbines is a main component for making maintenance decisions for this type of machine. In this paper, the faults detection approach based on fuzzy logic is applied for the vibrations monitoring of a gas turbine, in order to monitor their operating state by including the detection and occurrence of vibration faults, thus using determined fault indicators based on the input/output variables of the examined gas turbine. In this work, the investigation results of fuzzy fault detection approach applied on gas turbine vibration are presented, based on the actual data recorded in the different gas turbine operating modes. However, analysis of the defect detection results was performed in order to determine the influence of these vibration defects on the deferent operating modes of the examined machine. This makes it possible to find the causes of failures and then to deduce the actions to follow the operational safety of the examined turbine.
    Źródło:
    Diagnostyka; 2020, 21, 4; 67-77
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring and control of energy management system for fuel cell hybrid in electrical vehicle using fuzzy approach
    Autorzy:
    Tifour, Benali
    Boukhnifer, Moussa
    Hafaifa, Ahmed
    Tanougast, Camel
    Powiązania:
    https://bibliotekanauki.pl/articles/327624.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    energy monitoring
    fuel cell hybrid
    particle swarm
    fuel economy
    fuzzy logic
    monitorowanie energii
    zarządzanie energią
    ogniwo paliwowe
    zużycie paliwa
    logika rozmyta
    Opis:
    Recently, the reduction of fuels consumption is a global challenge, in particular for significant investments in the automotive sector, in order to optimize and control the parameters involved for the partial or total electrification of vehicles. Thereby, the energy management system remains the axis of progress for the development of fuel cell hybrid electric vehicles. The fuzzy controller has been widely adopted for energy monitoring, where the determination of its parameters is still challenging. In this work, this problem is investigated through a secondary development of a fuzzy energy monitoring system based on the Advisor platform and particle swarm optimization. The latter is used to determine, for different driving conditions, the best parameters that increase the fuel economy and reduce the battery energy use. As a result, five tuned fuzzy energy monitoring system models with five sets of parameters are obtained. Evaluation results confirm the effectiveness of this strategy, they also show slight differences between them in terms of fuel economy, battery state of charge variations, and overall system efficiency. However, the fuzzy energy monitoring system tuned under multiple conditions is the only one that can guarantee the minimum of the state of charge variations, no matter the driving conditions.
    Źródło:
    Diagnostyka; 2020, 21, 3; 15-29
    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
      Wyświetlanie 1-3 z 3

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