Documents disponibles écrits par cet auteur

Application of an optimal tuner selection approach for on-board self-tuning engine models / Donald L. Simon in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 134 N° 4 (Avril 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 4 (Avril 2012) . - 11 p. Titre : Application of an optimal tuner selection approach for on-board self-tuning engine models Type de document : texte imprimé Auteurs : Donald L. Simon, Auteur ; Jeffrey B. Armstrong, Auteur ; Sanjay Garg, Auteur Année de publication : 2012 Article en page(s) : 11 p. Note générale : Génie mécanique Langues : Anglais (eng) Mots-clés : Aerospace  engines  Aircraft  control  Closed  loop  systems  Dynamic  response  Kalman  filters  Mean  square  error  methods  Open  loop  systems  Optimisation  Self-adjusting  systems  State  estimation Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000004 [...] [article] Application of an optimal tuner selection approach for on-board self-tuning engine models [texte imprimé] / Donald L. Simon, Auteur ; Jeffrey B. Armstrong, Auteur ; Sanjay Garg, Auteur . - 2012 . - 11 p. Génie mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 4 (Avril 2012) . - 11 p. Mots-clés : Aerospace  engines  Aircraft  control  Closed  loop  systems  Dynamic  response  Kalman  filters  Mean  square  error  methods  Open  loop  systems  Optimisation  Self-adjusting  systems  State  estimation Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000004 [...]

A data filter for identifying steady-state operating points in engine flight data for condition monitoring applications / Donald L. Simon in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 133 N° 7 (Juillet 2011) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 133 N° 7 (Juillet 2011) . - 08 p. Titre : A data filter for identifying steady-state operating points in engine flight data for condition monitoring applications Type de document : texte imprimé Auteurs : Donald L. Simon, Auteur ; Jonathan S. Litt, Auteur Année de publication : 2011 Article en page(s) : 08 p. Note générale : Turbines à gaz Langues : Anglais (eng) Mots-clés : Aerospace  engineering  Aerospace  engines  Condition  monitoring  Data  handling  Helicopters Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ00013300 [...] [article] A data filter for identifying steady-state operating points in engine flight data for condition monitoring applications [texte imprimé] / Donald L. Simon, Auteur ; Jonathan S. Litt, Auteur . - 2011 . - 08 p. Turbines à gaz Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 133 N° 7 (Juillet 2011) . - 08 p. Mots-clés : Aerospace  engineering  Aerospace  engines  Condition  monitoring  Data  handling  Helicopters Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ00013300 [...]

Optimal tuner selection for Kalman filter-based aircraft engine performance estimation / Donald L. Simon in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 132 N° 3 (Mars 2010) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 132 N° 3 (Mars 2010) . - 10 p. Titre : Optimal tuner selection for Kalman filter-based aircraft engine performance estimation Type de document : texte imprimé Auteurs : Donald L. Simon, Auteur ; Sanjay Garg, Auteur Année de publication : 2010 Article en page(s) : 10 p. Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Aerospace  engines  Circuit  tuning  Iterative  methods  Kalman  filters  Mean  square  error  methods  Search  problems Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000132000003 [...] [article] Optimal tuner selection for Kalman filter-based aircraft engine performance estimation [texte imprimé] / Donald L. Simon, Auteur ; Sanjay Garg, Auteur . - 2010 . - 10 p. Génie Mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 132 N° 3 (Mars 2010) . - 10 p. Mots-clés : Aerospace  engines  Circuit  tuning  Iterative  methods  Kalman  filters  Mean  square  error  methods  Search  problems Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000132000003 [...]