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    Wyświetlanie 1-4 z 4
    Tytuł:
    Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems
    Autorzy:
    Hadroug, N.
    Hafaifa, A.
    Kouzou, A.
    Chaibet, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/328706.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    faults detection
    gas turbine
    dynamic behaviour
    adaptive network based fuzzy inference systems
    ANFIS
    detekcja błędów
    turbina gazowa
    zachowanie dynamiczne
    Opis:
    The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection.
    Źródło:
    Diagnostyka; 2016, 17, 4; 3-17
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches
    Autorzy:
    Patel, M. G. C.
    Krishna, P.
    Parappagoudar, M. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/380567.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    squeeze casting process
    secondary dendrite arm spacing
    fuzzy logic
    adaptive network based fuzzy
    interface system
    prasowanie stopu
    odległość ramion dendrytów
    logika rozmyta
    system interfejsowy
    Opis:
    The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.
    Źródło:
    Archives of Foundry Engineering; 2015, 15, 1; 51-68
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
    Autorzy:
    Kumar, D. T.
    Soleimani, H.
    Kannan, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/329809.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    artificial neural network
    adaptive network based fuzzy
    inference system
    closed loop supply chain
    forecasting methods
    fuzzy neural network
    sztuczna sieć neuronowa
    system wnioskowania
    metoda prognozowania
    sieć neuronowa rozmyta
    Opis:
    Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 669-682
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid model for modelling the salinity of the Tafna River in Algeria
    Hybrydowy model służący modelowaniu zasolenia rzeki Tafna w Algierii
    Autorzy:
    Houari, Khemissi
    Hartani, Tarik
    Remini, Boualem
    Lefkir, Abdelouhab
    Abda, Leila
    Heddam, Salim
    Powiązania:
    https://bibliotekanauki.pl/articles/292367.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    Adaptive-Network-Based Fuzzy Inference System (ANFIS)
    hybrid model
    neuro-fuzzy
    salinity
    salt flow
    Tafna River
    model hybrydowy
    przepływ soli
    rzeka Tafna
    system neuronowo-rozmyty
    system wnioskowania rozmytego (ANFIS)
    zasolenie
    Opis:
    In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).
    W pracy badano zdolność systemu wnioskowania rozmytego opartego na adaptacyjnej sieci (ANFIS) do przewidywania zasolenia rzeki Tafna. Do trenowania, oceny i testowania modelu hybrydowego wykorzystano serie pomiarów dobowych przepływów płynu i stężeń soli ze stacji pomiarowej w Pierre du Chat (160801). Dokładność wyników testowano za pomocą: współczynnika determinacji (R2), współczynnika wydajności Nasha–Sutcliffe’a (E), pierwiastka średniego błędu kwadratowego (RSR) i technik graficznych. Model dał zadowalające wyniki i wykazywał dobrą zgodność między danymi obserwowanymi a przewidywanymi: R2 (88% w przypadku uczenia sieci, 78.01% walidacji i 80.00% testowania), E (85.84% w przypadku uczenia sieci, 82.51% walidacji i 78.17% testowania) i RSR (2% w przypadku uczenia sieci, 10% walidacji i 49% testowania).
    Źródło:
    Journal of Water and Land Development; 2019, 40; 127-135
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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