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    Wyświetlanie 1-4 z 4
    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:
    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:
    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:
    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 1-4 z 4

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