Documents disponibles écrits par cet auteur

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]  in Transactions 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  analysis 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  analysis 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 [...]

Development of a statistical methodology for gas turbine prognostics / Nicola Puggina in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 134 N° 2 (Février 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 2 (Février 2012) . - 09 p. Titre : Development of a statistical methodology for gas turbine prognostics Type de document : texte imprimé Auteurs : Nicola Puggina, Auteur ; Mauro Venturini, Auteur Année de publication : 2012 Article en page(s) : 09 p. Note générale : Génie mécanique Langues : Anglais (eng) Mots-clés : Gas  turbines  Monte  Carlo  methods  Reliability  Sensitivity  analysis  Statistical  analysis Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : To optimize both production and maintenance, from both a technical and an economical point of view, it would be advisable to predict the future health condition of a system and of its components, starting from field measurements taken in the past. For this purpose, this paper presents a methodology, based on the Monte Carlo statistical method, which aims to determine the future operating state of a gas turbine. The methodology allows the system future availability to be estimated, to support a prognostic process based on past historical data trends. One of the most innovative features is that the prognostic methodology can be applied to both global and local performance parameters, as, for instance, machine specific fuel consumption or local temperatures. First, the theoretical background for developing the prognostic methodology is outlined. Then, the procedure for implementing the methodology is developed and a simulation model is set up. Finally, different degradation-over-time scenarios for a gas turbine are simulated and a sensitivity analysis on methodology response is carried out, to assess the capability and the reliability of the prognostic methodology. The methodology proves robust and reliable, with a prediction error lower than 2%, for the availability associated with the next future data trend. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000002 [...] [article] Development of a statistical methodology for gas turbine prognostics [texte imprimé] / Nicola Puggina, Auteur ; Mauro Venturini, Auteur . - 2012 . - 09 p. Génie mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 2 (Février 2012) . - 09 p. Mots-clés : Gas  turbines  Monte  Carlo  methods  Reliability  Sensitivity  analysis  Statistical  analysis Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : To optimize both production and maintenance, from both a technical and an economical point of view, it would be advisable to predict the future health condition of a system and of its components, starting from field measurements taken in the past. For this purpose, this paper presents a methodology, based on the Monte Carlo statistical method, which aims to determine the future operating state of a gas turbine. The methodology allows the system future availability to be estimated, to support a prognostic process based on past historical data trends. One of the most innovative features is that the prognostic methodology can be applied to both global and local performance parameters, as, for instance, machine specific fuel consumption or local temperatures. First, the theoretical background for developing the prognostic methodology is outlined. Then, the procedure for implementing the methodology is developed and a simulation model is set up. Finally, different degradation-over-time scenarios for a gas turbine are simulated and a sensitivity analysis on methodology response is carried out, to assess the capability and the reliability of the prognostic methodology. The methodology proves robust and reliable, with a prediction error lower than 2%, for the availability associated with the next future data trend. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000002 [...]

Prediction reliability of a statistical methodology for gas turbine prognostics / Mauro Venturini in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 134 N° 10 (Octobre 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 10 (Octobre 2012) . - 09 p. Titre : Prediction reliability of a statistical methodology for gas turbine prognostics Type de document : texte imprimé Auteurs : Mauro Venturini, Auteur ; Nicola Puggina, Auteur Année de publication : 2012 Article en page(s) : 09 p. Note générale : gas turbines Langues : Anglais (eng) Mots-clés : gas  turbines;  time  evolution;  Monte  Carlo  statistical  method Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today's competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000010 [...] [article] Prediction reliability of a statistical methodology for gas turbine prognostics [texte imprimé] / Mauro Venturini, Auteur ; Nicola Puggina, Auteur . - 2012 . - 09 p. gas turbines Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 10 (Octobre 2012) . - 09 p. Mots-clés : gas  turbines;  time  evolution;  Monte  Carlo  statistical  method Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today's competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000010 [...]