[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 [...] |
|
Affiner la rechercheDevelopment 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)
