State initialization for gas turbine engine performance diagnostics

• US 20080126012A1
• Filed: 11/29/2006
• Published: 05/29/2008
• Est. Priority Date: 11/29/2006
• Status: Abandoned Application

• Current Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Volponi, Allan J.
• Application Number:
  US11/605,724
• Publication Number:
  US 20080126012A1
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: G05B 13/04 involving the use of models or simulators
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods

Abstract

A state-based, gas turbine engine initialization method and system is described that allows the true level of performance to be tracked thereby enabling engine-to-engine performance comparisons. The method performs an initialization process for performing gas turbine Module Performance Analysis (MPA) via a recursive state estimation. Included is a description of data validity measures and maintenance impact and accommodation on the initialized, or re-initialized, state.

Claims

1. An initialization method for ascertaining the performance levels of gas turbine engine modules comprising:

acquiring a predetermined number of in-flight data samples corresponding to a predetermined number of engine parameters;

performing an anomalous data sample screen on the data samples; and

performing a recursive state estimation on the predetermined number of in-flight data samples, wherein the state estimation outputs a baseline initial state for each of the predetermined number of engine parameters.

2. The method according to 1 further comprising acquiring gas turbine engine test stand data for use as a priori data when performing the recursive state estimation.

3. The method according to claim 2 wherein the anomalous data sample screen further comprises screening for bimodal distributions and data sample outliers.

4. The method according to claim 3 wherein the anomalous data sample screen further comprises using histograms and graphical analysis to test for the presence of anomalous data.

5. The method according to claim 3 wherein the predetermined number of data samples corresponding to the engine parameters are arranged as vectors with each vector corresponding to a sample number.

6. The method according to claim 5 further comprising removing a data sample vector if any anomalous data is found.

7. The method according to claim 6 wherein the number of data samples is in a range of from 15 to 30.

8. The method according to claim 7 further comprising adding additional data samples if after removing the anomalous data samples, the number of data samples is less than the predetermined number of data samples.

9. The method according to claim 8 wherein adding additional data samples further comprises additional flights.

10. The method according to claim 9 wherein the recursive state estimation is a predictor-collector.

11. The method according to claim 10 wherein the gas turbine engine modules comprise a fan compressor, a low pressure compressor, a high pressure compressor, a high pressure turbine, and a low pressure turbine.

12. The method according to claim 11 wherein each of the gas turbine engine modules further comprise at least two performance parameters.

13. The method according to claim 12 wherein two performance parameters comprise adiabatic efficiency Δ

η and

flow Δ

A.

14. The method according to claim 13 wherein the flow performance parameter is a result of turbine nozzle area change.