Process for adapting measurement suite configuration for gas turbine performance diagnostics

• US 20080177505A1
• Filed: 01/24/2007
• Published: 07/24/2008
• Est. Priority Date: 01/24/2007
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Volponi, Allan J.
• Granted Patent: US 7,441,448 B2
• Time in Patent Office: Days
• US Class Current: 702/185
• CPC Class Codes: F05B 2260/80 Diagnostics
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0297 Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results

Abstract

A method and system is described that estimates gas turbine engine module performance changes from gas path measurement data using a linear predictor-corrector estimation process. A measurement configuration matrix M_C and a fault configuration F_C matrix are assembled. The configuration matrices M_C and F_C are used to adapt a typical linear predictor-corrector type of performance estimation process to a given measurement suite and are ideally suited for linear estimation systems such as those using a Kalman filter.

Claims

1. A method for adapting measurements from a gas turbine engine for use in performance tracking comprising:

selecting a default engine configuration for the gas turbine engine and its application;

acquiring gas path data samples corresponding to a predetermined number of measured parameters;

deriving performance parameters for the gas turbine engine;

comparing the measured parameters and performance parameters at time k with predefined thresholds to determine the quality and/or availability of each parameter, wherein parameters determined to be questionable are defined as not present for performance tracking;

determining a measurement configuration matrix M_C based on the default engine configuration and present measured parameters Z, and a fault configuration matrix F_C based on the default engine configuration and present performance parameters x₀; and

adapting performance tracking methods using the measurement configuration matrix M_C and fault configuration matrix F_C.

2. The method according to claim 1 wherein the measurement configuration matrix M_C is an identity matrix of size m where m=number of measured parameters.

3. The method according to claim 2 wherein the fault configuration matrix F_C is an identity matrix of size n where n=the number of performance faults being estimated.

4. The method according to claim 3 further comprising performing performance tracking calculations using a least squares or generalized least squares estimation calculation.

5. The method according to claim 4 wherein the least squares estimation is a Kalman filter.

6. The method according to claim 5 wherein the Kalman filter is an adaptive Kalman filter.

7. The method according to claim 6 wherein a zero entry in the measurement configuration matrix M_C main diagonal represents a questionable or missing parameter measurement.

8. The method according to claim 7 wherein a zero entry in the fault configuration matrix F_C main diagonal represents those faults whose estimation is affected by the loss of a corresponding measurement parameter(s) that is used to calculate the performance estimate.

9. The method according to claim 8 further comprising adapting an influence coefficient matrix H using the measurement configuration M_C and fault configuration F_C matrices, creating a modified influence coefficient matrix {tilde over (H)}.

10. The method according to claim 9 further comprising adapting a measurement sensor nonrepeatability standard deviation S using the measurement configuration M_C matrix and adapted influence coefficient matrix {tilde over (H)}, creating a modified measurement covariance matrix {tilde over (R)}.

11. The method according to claim 10 further comprising adapting a Kalman gain matrix {tilde over (D)} using the modified influence coefficient matrix {tilde over (H)} and modified measurement covariance matrix {tilde over (R)}.

12. The method according to claim 11 further comprising calculating a performance delta {circumflex over (x)} using the adapted Kalman gain matrix {tilde over (D)}, the adapted influence coefficient matrix {tilde over (H)}, present measured parameters Z, and present performance parameters x₀.

13. A system for adapting measurements from a gas turbine engine for use in performance tracking comprising:

means for selecting a default engine configuration for the gas turbine engine and its application;

means for acquiring gas path data samples corresponding to a predetermined number of measured parameters;

means for deriving performance parameters for the gas turbine engine;

means for comparing the measured parameters and performance parameters at time k with predefined thresholds to determine the quality and/or availability of each parameter, wherein parameters determined to be questionable are defined as not present for performance tracking;

means for determining a measurement configuration matrix M_C based on the default engine configuration and present measured parameters Z, and a fault configuration matrix F_C based on the default engine configuration and present performance parameters x; and

means for adapting performance tracking methods using the measurement configuration matrix M_C and fault configuration matrix F_C.

14. The system according to claim 13 wherein the measurement configuration matrix M_C is an identity matrix of size m where m=number of measured parameters.

15. The system according to claim 14 wherein the fault configuration matrix F_C is an identity matrix of size n where n=the number of performance faults being estimated.

16. The system according to claim 15 further comprising means for performing performance tracking calculations using a least squares or generalized least squares estimation calculation.

17. The system according to claim 16 wherein the least squares estimation is a Kalman filter.

18. The system according to claim 17 wherein the Kalman filter is an adaptive Kalman filter.

19. The system according to claim 18 wherein a zero entry in the measurement configuration matrix M_C main diagonal represents a questionable or missing parameter measurement.

20. The system according to claim 19 wherein a zero entry in the fault configuration matrix F_C main diagonal represents those faults whose estimation is affected by the loss of a corresponding measurement parameter(s) that is used to calculate the performance estimate.

21. The system according to claim 20 further comprising means for adapting an influence coefficient matrix H using the measurement configuration M_C and fault configuration F_C matrices, creating a modified influence coefficient matrix {tilde over (H)}.

22. The system according to claim 21 further comprising means for adapting a measurement sensor nonrepeatability standard deviation S using the measurement configuration M_C matrix and adapted influence coefficient matrix {tilde over (H)}, creating a modified measurement covariance matrix {tilde over (R)}.

23. The system according to claim 22 further comprising means for adapting a Kalman gain matrix {tilde over (D)} using the modified influence coefficient matrix {tilde over (H)} and modified measurement covariance matrix {tilde over (R)}.

24. The system according to claim 23 further comprising means for calculating a performance delta {circumflex over (x)} using the adapted Kalman gain matrix {tilde over (D)}, the adapted influence coefficient matrix {tilde over (H)}, present measured parameters Z, and present performance parameters x₀.