Model based engine inlet condition estimation

• US 8,720,258 B2
• Filed: 09/28/2012
• Issued: 05/13/2014
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Meisner, Richard P., Britten, Alexandra I., Poth, Stefan M. Jr., Karpman, Boris
• Primary Examiner(s): Kirkland, III, Freddie
• Application Number:
  US13/631,359
• Publication Number:
  US 20140090456A1
• Time in Patent Office: 592 Days
• Field of Search: 731/120.1
• US Class Current: 73/112.01
• CPC Class Codes: G01M 15/14 Testing gas-turbine engines or jet-propulsion engines
• 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 gas turbine engine inlet sensor fault detection and accommodation system comprises an engine model, an engine parameter comparison block, an inlet condition estimator, control laws, and a fault detection and accommodation system. The engine model is configured to produce a real-time model-based estimate of engine parameters. The engine parameter comparison block is configured to produce residuals indicating the difference between the real-time model-based estimate of engine parameters and sensed values of the engine parameters. The inlet condition estimator is configured to iteratively adjust an estimate of inlet conditions based on the residuals. The control laws are configured to produce engine control parameters for control of gas turbine engine actuators based on the inlet conditions. The fault detection and accommodation system is configured to detect faults in inlet condition sensors, select sensed inlet conditions for use by the control laws in the event of no fault, and select estimated inlet conditions for use by the control laws in the event of inlet condition sensor fault.

Claims

1. A gas turbine engine inlet sensor fault detection and accommodation system comprising:

an engine model configured to produce a real-time model-based estimate of engine parameters;

an engine parameter comparison block configured to produce residuals indicating the difference between the real-time model-based estimate of engine parameters and sensed values of the engine parameters;

an inlet condition estimator configured to iteratively adjust an estimate of inlet conditions based on the residuals;

control laws configured to produce engine control parameters for control of gas turbine engine actuators based on the inlet conditions; and

a fault detection and accommodation system configured to detect faults in inlet condition sensors, select sensed inlet conditions for use by the control laws in the event of no fault, and select estimated inlet conditions for use by the control laws in the event of inlet condition sensor fault.

2. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the engine model is configured to the produce real-time model-based estimate engine parameters based on a previous iteration estimate of inlet conditions, and based on engine control parameters.

3. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the inlet conditions include compressor inlet temperature and compressor inlet pressure.

4. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the engine model receives engine control parameters, and updates for a next timestep using the engine model.

5. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the engine control parameters include at least one of a rotor speed, a combustor pressure, and an exhaust gas temperature.

6. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the fault detection and accommodation system detects faults by flagging a fault when a value or a rate of change of a value of at least one of the sensed inlet conditions falls outside of a specified range.

7. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the fault detection and accommodation system detects faults by flagging a fault when the sensed inlet conditions differ by more than a threshold value from the estimated inlet conditions.

8. The gas turbine engine inlet sensor fault detection and accommodation system of claim 1, wherein the inlet condition estimator iteratively adjusts the estimate of the inlet conditions by adjusting a previous timestep estimate of the inlet conditions by a function of the residuals and a realtime variable vector gain.

9. The gas turbine engine inlet sensor fault detection and accommodation system of claim 8, wherein the realtime variable vector gain is retrieved from a lookup table.

10. The gas turbine engine inlet sensor fault detection and accommodation system of claim 8, wherein the realtime variable vector gain is estimated using the engine model.

11. A method for accommodating faults among inlet sensors on a gas turbine engine, the method comprising:

sensing engine inlet conditions at an inlet of the gas turbine engine;

iteratively producing a real-time model-based estimate of engine inlet conditions;

identifying faults in the inlet sensors; and

utilizing the estimated engine inlet conditions to produce engine control parameters in the event of a fault, and the sensed engine inlet conditions otherwise.

12. The method of claim 11, wherein identifying faults in the inlet sensors comprises flagging a fault whenever a value of the sensed engine inlet conditions or a rate of change of the sensed engine inlet conditions falls outside of a predefined range.

13. The method of claim 12, wherein identifying faults in the inlet sensors comprises flagging a fault whenever a value of the sensed engine inlet conditions or a rate of change of the sensed engine inlet conditions falls outside of a predefined range in aggregate or on average over several timesteps of the method.

14. The method of claim 11, wherein identifying faults in the inlet sensors comprises flagging a fault whenever a value of the sensed engine inlet conditions differs from a corresponding value of the estimated inlet conditions by more than a predefined amount.

15. The method of claim 14, wherein identifying faults in the inlet sensors comprises flagging a fault whenever a value of the sensed engine inlet conditions differs from a corresponding value of the estimated inlet conditions by more than a predefined amount in aggregate or on average over several timesteps of the method.

16. The method of claim 11, wherein the gas turbine inlet conditions are gas turbine compressor inlet temperature and pressure.

17. The method of claim 11, wherein iteratively producing a real-time model-based estimate of engine inlet conditions comprises forming residuals from a difference between sensed engine parameters and real-time model-based estimates of corresponding engine parameters, and adjusting previous timestep estimates of inlet conditions based on the residuals and a vector gain.

18. The method of claim 17, wherein the vector gain is retrieved in real time from a lookup table.

19. The method of claim 17, wherein the vector gain is computed in real time using a gas turbine engine model.