Application of forecasting methodologies to predict gas turbine behavior over time / Andrea Cavarzere in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 134 N° 1 (Janvier 2012)  

Public ISBD [article]  inTransactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 1 (Janvier 2012) . - 08 p. Titre : Application of forecasting methodologies to predict gas turbine behavior over time Type de document : texte imprimé Auteurs : Andrea Cavarzere, Auteur ; Mauro Venturini, Auteur Année de publication : 2012 Article en page(s) : 08 p. Note générale : Génie mécanique Langues : Anglais (eng) Mots-clés : Bayes methods Failure analysis Forecasting theory Gas turbines Kalman filters Maintenance engineering Occupational safety Regression Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : The growing need to increase the competitiveness of industrial systems continuously requires a reduction of maintenance costs, without compromising safe plant operation. Therefore, forecasting the future behavior of a system allows planning maintenance actions and saving costs, because unexpected stops can be avoided. In this paper, four different methodologies are applied to predict gas turbine behavior over time: Linear and Nonlinear Regression, One Parameter Double Exponential Smoothing, Kalman Filter and Bayesian Forecasting Method. The four methodologies are used to provide a prediction of the time when a threshold value will be exceeded in the future, as a function of the current trend of the considered parameter. The application considers different scenarios which may be representative of the trend over time of some significant parameters for gas turbines. Moreover, the Bayesian Forecasting Method, which allows the detection of discontinuities in time series, is also tested for predicting system behavior after two consecutive trends. The results presented in this paper aim to select the most suitable methodology that allows both trending and forecasting as a function of data trend over time, in order to predict time evolution of gas turbine characteristic parameters and to provide an estimate of the occurrence of a failure. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000001 [...] [article] Application of forecasting methodologies to predict gas turbine behavior over time [texte imprimé] / Andrea Cavarzere, Auteur ; Mauro Venturini, Auteur . - 2012 . - 08 p. Génie mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 1 (Janvier 2012) . - 08 p. Mots-clés : Bayes methods Failure analysis Forecasting theory Gas turbines Kalman filters Maintenance engineering Occupational safety Regression Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : The growing need to increase the competitiveness of industrial systems continuously requires a reduction of maintenance costs, without compromising safe plant operation. Therefore, forecasting the future behavior of a system allows planning maintenance actions and saving costs, because unexpected stops can be avoided. In this paper, four different methodologies are applied to predict gas turbine behavior over time: Linear and Nonlinear Regression, One Parameter Double Exponential Smoothing, Kalman Filter and Bayesian Forecasting Method. The four methodologies are used to provide a prediction of the time when a threshold value will be exceeded in the future, as a function of the current trend of the considered parameter. The application considers different scenarios which may be representative of the trend over time of some significant parameters for gas turbines. Moreover, the Bayesian Forecasting Method, which allows the detection of discontinuities in time series, is also tested for predicting system behavior after two consecutive trends. The results presented in this paper aim to select the most suitable methodology that allows both trending and forecasting as a function of data trend over time, in order to predict time evolution of gas turbine characteristic parameters and to provide an estimate of the occurrence of a failure. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000001 [...]

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