System for gas turbine health monitoring data fusion

• US 20060047403A1
• Filed: 08/26/2004
• Published: 03/02/2006
• Est. Priority Date: 08/26/2004
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Volponi, Allan J., Wood, C. Bruce
• Granted Patent: US 7,769,507 B2
• Time in Patent Office: Days
• US Class Current: 701/100
• CPC Class Codes: F01D 17/02 Arrangement of sensing elements
• CPC Class Codes: F05D 2260/80 Diagnostics
• CPC Class Codes: F05D 2270/707 fuzzy logic
• CPC Class Codes: F05D 2270/709 with neural networks
• 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

An apparatus for assessing health of a device comprising a data alignment module for receiving a plurality of sensory outputs and outputting a synchronized data stream, an analysis module for receiving the synchronized data stream and outputting at least one device health feature, and a high level diagnostic feature information fusion module for receiving the at least one device health feature and outputting a device health assessment.

Claims

1. An apparatus for assessing health of a device comprising:

a data alignment module for receiving a plurality of sensory outputs and outputting a synchronized data stream;

an analysis module for receiving said synchronized data stream and outputting at least one device health feature; and

a high level diagnostic feature information fusion module for receiving said at least one device health feature and outputting a device health assessment.

2. The apparatus of claim 1 wherein said device is an engine.

3. The apparatus of claim 1 additionally comprising a fault isolation reasoner module for combining said engine health assessment with at least one additional data source to produce a recommended maintenance action.

4. The apparatus of claim 3 wherein said at least one additional data source is selected from the group consisting of maintainer observations, pilot observations, engine maintenance history, and Full Authority Digital Engine Control (FADEC) fault codes.

5. The apparatus of claim 1 additionally comprising a plurality of sensors for generating said sensory outputs.

6. The apparatus of claim 5 wherein said sensory outputs are selected from the group consisting of altitude, mach, ambient temperature, ambient pressure, temperature, pressure, gas flow speeds, oil temperatures, oil pressures, and oil quantities.

7. The apparatus of claim 5 wherein said sensors are selected from the group consisting of accelerometers, and oil debris monitors, oil condition monitors, electrostatic debris monitors, acoustic monitors, and eddy current sensors.

8. The apparatus of claim 5 additionally comprising at least one feature extraction unit for extracting a feature from at least one sensory output.

9. The apparatus of claim 1 wherein said analysis module is adapted to utilize models selected from the group consisting of State Variable Engine Models with Kalman observers, empirical engine models, physics based lubrication system models, empirical based lubrication system models, and engine component life models.

10. The apparatus of claim 1 wherein said analysis module is adapted to utilize constructs selected from the group consisting Kalman Filters, Artificial Neural Networks, and Fuzzy Logic based systems.

11. The apparatus of claim 1 wherein said high level diagnostic feature information fusion module is adapted to utilize Bayesian Belief Networks (BBN), and Fuzzy Belief Networks (FBN).

12. A method for assessing health of a device comprising the steps of:

receiving a plurality of sensory outputs and outputting a synchronized data stream;

receiving said synchronized data stream and outputting at least one device health feature; and

receiving said at least one device health feature and outputting a device health assessment.

13. The method of claim 12 wherein said receiving said synchronized data stream and outputting said at least one device health feature comprises outputting at least one engine health feature.

14. The method of claim 12 comprising the additional step of combining said engine health assessment with at least one external data source to produce a recommended maintenance action.

15. The method of claim 12 wherein said receiving said plurality of sensory outputs comprises the additional steps of:

commencing to receive said plurality of sensory outputs at a beginning of a window duration;

sampling at least one of said plurality of sensory outputs to produce a sampled data stream; and

outputting said synchronized data stream comprising said sampled data stream at an end of said window duration.

16. The method of claim 15 wherein said commencing to receive said plurality of sensory outputs comprises commencing to receive said plurality of sensory outputs at said beginning of said window duration extending for between 33 ms and 100 ms.

17. The method of claim 16 wherein said commencing to receive said plurality of sensory outputs comprises commencing to receive said plurality of sensory outputs at said beginning of said window duration extending for between 50 ms and 66 ms.

18. The method of claim 12 wherein said receiving said plurality of sensory outputs comprises receiving said plurality of sensory outputs selected from the group consisting of altitude, mach, ambient temperature, ambient pressure, temperature, pressure, gas flow speeds, oil temperatures, oil pressures, and oil quantities.

19. The method of claim 12 wherein said receiving said plurality of sensory outputs comprises receiving said plurality of sensory outputs from a plurality of sensors selected from the group consisting of accelerometers, and oil debris monitors, oil condition monitors, electrostatic debris monitors, acoustic monitors, and eddy current sensors.

20. The method of claim 12 wherein said receiving said synchronized data stream and outputting at least one engine health feature comprises performing analysis of said synchronized data stream by utilizing at least one model selected from the group consisting of State Variable Engine Models with Kalman observers, empirical engine models, physics based lubrication system models, empirical based lubrication system models, and engine component life models.

21. The apparatus of claim 12 wherein said receiving said synchronized data stream and outputting at least one engine health feature comprises performing analysis of said synchronized data stream by utilizing at least one construct selected from the group consisting Kalman Filters, Artificial Neural Networks, and Fuzzy Logic based systems.

22. An apparatus for assessing the health of a device, comprising:

a means for aligning data received from a plurality of sensors to produce a synchronized data stream;

a means for analyzing said synchronized data stream to produce at least one device health feature; and

a means for fusing high level diagnostic feature information with said at least one device health feature to produce a device health assessment.