Apparatus for monitoring gas turbine engine operation

• US 20020193933A1
• Filed: 08/20/2002
• Published: 12/19/2002
• Est. Priority Date: 11/02/2000
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Adibhatla, Sridhar, Ashby, Malcolm John
• Granted Patent: US 6,532,412 B2
• Time in Patent Office: Days
• US Class Current: 701/100
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: F02C 9/28 Regulating systems responsive to plant or ambient parameters, e.g. temperature, pressure, rotor speed F02C9/30 - F02C9/38, F02C9/44 take precedence
• CPC Class Codes: F05D 2260/80 Diagnostics
• CPC Class Codes: F05D 2260/81 Modelling or simulation
• CPC Class Codes: F05D 2260/82 Forecasts
• 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

Abstract

A model-based trending process for a gas turbine engine that generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition is described. The engine includes a plurality of sensors that are responsive to engine operations. The trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity. Engine health parameters are estimated and adjusted in a model for component diagnostics and fault detection and isolation. The trend parameters generated are retained.

Claims

1. A method for identifying trends in engine operation, the engine having a plurality of sensors responsive to engine operations, said method comprising the steps of:

obtaining outputs from at least some of the sensors when the engine is operating;

obtaining ambient flight condition data; and

generating normalized engine trend parameters using the engine sensor data and ambient flight condition data.

2. A method in accordance with claim 1 wherein said step of generating normalized engine trend parameters further comprises the steps of:

using an engine model to generate additional trend parameters;

adjusting engine component efficiencies and flow functions in the engine model to account for engine faults, engine quality, and deterioration effects;

using the engine model to generate normalized engine sensor trend parameters; and

using the engine model to generate additional trend parameters or normalized virtual sensors.

3. A method in accordance with claim 2 wherein said step of generating normalized engine trend parameters further comprising the step of normalizing engine trend parameters in real-time during engine operation.

4. A method in accordance with claim 2 wherein said step of generating normalized engine trend parameters further comprises the step of using at least one of a physics-based, regression-fit, or a neural-net engine model to normalize engine trend parameters.

5. A method according to claim 2 wherein said step of adjusting engine component efficiencies and flow functions further comprises the step of using a parameter estimation algorithm to adjust engine component efficiencies and flow functions.

6. A method according to claim 5 wherein said step of using a parameter estimation algorithm further comprises the step of using at least one of a linear regression scheme or a Kalman filter to adjust engine component efficiencies and flow functions.

7. Apparatus for trending gas turbine engine operation, the engine having a plurality of sensors responsive to engine operations, said apparatus comprising a processor programmed to:

obtain engine sensor data and ambient flight condition data; and

generate and normalize engine trend parameters using the engine sensor data and ambient flight condition data.

8. Apparatus in accordance with claim 7 wherein to generate and normalize trend parameters, said processor further programmed to use an engine model to generate and normalize trend parameters.

9. Apparatus in accordance with claim 7 wherein said processor further programmed to generate and normalize engine trend parameters in real-time.

10. Apparatus in accordance with claim 7 wherein said processor further programmed to estimate sensed parameters and computed virtual sensor parameters using an engine model.

11. Apparatus in accordance with claim 7 wherein said processor further programmed to retain the trend parameters generated.

12. Apparatus in accordance with claim 7 wherein said processor further programmed to use gas turbine engine quality and deterioration effects to normalize engine trend parameters.

13. Apparatus in accordance with claim 7 wherein said processor further programmed to use at least one of a physics-based, regression-fit, or a neural-net engine model to normalize engine trend parameters.

15. A system in accordance with claim 14 wherein said processor further programmed to generate and normalize engine trend parameters in real-time during engine operation.

16. A system in accordance with claim 15 wherein said processor further programmed to adjust engine component efficiencies and flow functions in said engine model to account for engine faults, engine quality, and deterioration effects.

17. A system in accordance with claim 15 wherein said engine model is at least one of a physics-based, regression-fit, or a neural-net engine model.

18. A system in accordance with claim 15 wherein said processor further programmed to use a parameter estimation algorithm to adjust engine component efficiencies and flow functions.

19. A system in accordance with claim 18 wherein the parameter estimation algorithm is at least one of a linear regression scheme or a Kalman filter.

14. A trending system for trending operation of a gas turbine engine, the engine having a plurality of sensors responsive to engine operations, said system comprising:

an engine model unit coupled to receive engine sensor data and ambient flight condition data input from the sensors; and

a processor programmed to generate and normalize engine trend parameters using the engine sensor data and ambient flight condition data obtained.