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Wyszukujesz frazę "Doukovska, L. A." wg kryterium: Autor

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics
    Autorzy:
    Hadjiski, M. B.
    Doukovska, L. A.
    Kojnov, S. L.
    Powiązania:
    https://bibliotekanauki.pl/articles/226410.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technical diagnosis
    fault diagnosis
    predictive maintenance
    manufacturing execution system (MES)
    enterprise resource planning (ERP)
    Opis:
    Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.
    Źródło:
    International Journal of Electronics and Telecommunications; 2012, 58, 4; 351-356
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Predictive Maintenance Sensors Placement by Combinatorial Optimization
    Autorzy:
    Borissova, D. I.
    Mustakerov, I. C.
    Doukovska, L. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/227278.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    predictive maintenance
    optimal sensors placement
    combinatorial optimization
    Opis:
    The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential problem in predictive maintenance monitoring is the optimal sensor placement. The paper addresses that problem by using mixed integer linear programming tasks solving. The proposed optimal sensors location approach is based on the difference between sensor information if sensor is present and information calculated by linear interpolation if sensor is not present. The tasks results define the optimal sensors locations for a given number of sensors. The results of chosen sensors locations give as close as possible repeating the curve of structure dynamic response function. The proposed approach is implemented in an algorithm for predictive maintenance and the numerical results indicate that together with intelligent signal processing it could be suitable for practical application.
    Źródło:
    International Journal of Electronics and Telecommunications; 2012, 58, 2; 153-158
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems
    Autorzy:
    Koprinkova-Hristova, P. D.
    Hadjiski, M. B.
    Doukovska, L. A.
    Beloreshki, S. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/226304.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technical diagnosis
    Thermal Power Plant (TPP)
    Recurrent Neural Networks (RNN)
    distributed control system (DCS)
    predictive maintenance
    Opis:
    In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant - a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose, we compared two types of recurrent neural networks: historical Elman architecture and a recently developed kind of RNN named Echo stet networks (ESN). The preliminary investigations showed better approximation and faster training abilities of ESN in comparison to the Elman network. Direction of future work will be increasing of predications time horizon and inclusion of our predictor at lower level of a complex predictive maintenance system.
    Źródło:
    International Journal of Electronics and Telecommunications; 2011, 57, 3; 401-406
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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