HYBRID FAULT ISOLATION SYSTEM UTILIZING BOTH MODEL-BASED AND EMPIRICAL COMPONENTS

• US 20100017092A1
• Filed: 07/16/2008
• Published: 01/21/2010
• Est. Priority Date: 07/16/2008
• Status: Abandoned Application

• Current Assignee: United Technologies Corporation (Rtx Corporation)
• Original Assignee: United Technologies Corporation (Rtx Corporation)
• Inventors: Butler, Steven Wayne
• Application Number:
  US12/174,015
• Publication Number:
  US 20100017092A1
• Time in Patent Office: Days
• US Class Current: 701/100
• CPC Class Codes: B64D 2045/0085 Devices for aircraft health monitoring, e.g. monitoring flutter or vibration
• CPC Class Codes: F01D 21/003 Arrangements for testing or measuring for measuring vibrations G01H
• CPC Class Codes: F05D 2270/44 active, predictive, or anticipative
• CPC Class Codes: F05D 2270/708 with comparison tables
• CPC Class Codes: F05D 2270/709 with neural networks

Abstract

A method of operating and a fault diagnosis system compares readings to predicted faults using a model-based component, and a database of previous actual fault examples. A predicted fault is provided to an output based upon a combination of both the model-based component and the actual fault examples.

Claims

1. A fault diagnosis system comprising:

a system for taking in readings from a plurality of sensors associated with a gas turbine engine;

said plurality of sensors providing readings to the system, said system comparing said readings to predicted faults using a model-based fault isolation system, and a database of previous actual fault examples being queried to compare said readings; and

said fault isolation system providing a predicted fault to an output based upon both said model-based fault isolation system and said actual fault examples.

2. The system as set forth in claim 1, wherein weights are calculated for both the model-based fault predictions and the actual fault examples, and the weights are combined to provide a total weight for each of a plurality of potential faults.

3. The system as set forth in claim 2, wherein a combined weight for each of said plurality of potential faults is calculated in a loop until all potential faults have weights calculated.

4. The system as set forth in claim 3, wherein probabilities are generated based upon the combined weights from each of the potential fault examples.

5. The system as set forth in claim 4, wherein a most likely fault is sent as an output after the probabilities have been determined.

6. The system as set forth in claim 5, wherein the most likely fault is displayed to a maintenance personnel.

7. The system as set forth in claim 1, wherein the system determines the predicted fault utilizing the following equation:

8. The system as set forth in claim 7, wherein the system further determines the predicted fault utilizing the following equation:

9. A method of operating a fault diagnosis system including the steps of:

(a) taking sensor readings from a gas turbine engine;

(b) providing the readings to a system, said system comparing said readings to predicted faults using a model-based fault isolation system, and a database of previous actual fault examples being queried to compare said readings; and

(c) providing a predicted fault to an output based upon both said model-based fault isolation system and said actual fault examples.

10. The method as set forth in claim 9, wherein weights are calculated for both the model-based fault predictions and the actual fault examples, and the weights are combined to provide a total weight for each of a plurality of potential faults.

11. The method as set forth in claim 10, wherein a combined weight for each of said plurality of potential faults is calculated in a loop until all potential faults had weights calculated.

12. The method as set forth in claim 11, wherein probabilities are generated based upon the combined weights from each of the potential fault examples.

13. The method as set forth in claim 12, wherein a most likely fault is sent as an output after the probabilities have been determined.

14. The method as set forth in claim 13, wherein the most likely fault is displayed to a maintenance personnel.

15. The method as set forth in claim 9, wherein the fault is predicted utilizing the following formula: