Methodology for temporal fault event isolation and identification

• US 20040153815A1
• Filed: 10/31/2002
• Published: 08/05/2004
• Est. Priority Date: 10/31/2002
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Volponi, Allan J.
• Granted Patent: US 7,233,884 B2
• Time in Patent Office: Days
• US Class Current: 714/37
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure

Abstract

A method of fault event identification which comprises the steps of receiving a plurality of parameter measurements, performing single fault isolation to establish one of the plurality of parameter measurements as an event start, one of the plurality of parameter measurements as an event detect, and one of the parameter measurements as an event end wherein a latency period extends from the event start to the event detect and a blackout period extends from the event start to the event end, performing multiple fault isolation to establish a first trend line for the plurality of parameter measurements prior to the blackout period and after the blackout period, reprocessing the parameter measurements in the latency period, processing the parameter measurements in the blackout period, calculating a model IC from the reprocessed parameter measurements, calculating a plurality of single fault vectors, calculating an estimate for each of the single fault vectors, calculating a normalized error for each of said plurality of single fault vectors, calculating a polarized error term for each of the plurality of single fault vectors, and selecting the single fault vector with smallest of the calculated normalized errors.

Claims

1. A method of fault event identification, comprising the steps of:

receiving a plurality of parameter measurements;

performing single fault isolation to establish one of said plurality of parameter measurements as an event start, one of said plurality of parameter measurements as an event detect, and one of said parameter measurements as an event end wherein a latency period extends from said event start to said event detect and a blackout period extends from said event start to said event end;

performing multiple fault isolation to establish a first trend line for said plurality of parameter measurements prior to said blackout period and after said blackout period;

reprocessing said parameter measurements in said latency period;

processing said parameter measurements in said blackout period;

calculating a model IC from aero-thermal model simulation of the engine;

calculating a plurality of single fault vectors;

calculating an estimate for each of said single fault vectors;

calculating a normalized error for each of said plurality of single fault vectors;

calculating a polarized error term for each of said plurality of single fault vectors; and

selecting said single fault vector with smallest of said calculated normalized errors.

2. The method of claim 1 wherein each of said plurality of parameter measurements comprises a differential of a current measurement and an immediately preceding measurement.

3. The method of claim 2 wherein each of said plurality of parameter measurement is a percentage differential.

4. The method of claim 1 wherein said performing single fault isolation comprises performing a statistical analysis.

5. The method of claim 1, wherein said performing single fault isolation comprises applying Kalman Filter parameter estimation.

6. The method of claim 1, wherein said plurality of parameter measurements are derived from an engine.

7. The method of claim 1, wherein said reprocessing said parameter measurements comprises the steps of:

computing a linear regression line from said plurality of parameter measurements comprising said latency period; and

recomputing each of said plurality of measurements comprising said latency period as a difference between said linear regression line and an average level prior to said event start.

9. The method of claim 6 comprising the additional steps of:

selecting the smaller of said smallest normalized error for each of said plurality of single fault vectors and said smallest normalized error for each of said plurality of double fault vectors.

8. A method of fault event identification, comprising the steps of:

receiving a plurality of parameter measurements;

performing single fault isolation to establish one of said plurality of parameter measurements as an event start, one of said plurality of parameter measurements as an event detect, and one of said parameter measurements as an event end wherein a latency period extends from said event start to said event detect and a blackout period extends from said event start to said event end;

performing multiple fault isolation to establish a first trend line for said plurality of parameter measurements prior to said blackout period and after said blackout period;

reprocessing said parameter measurements in said latency period;

processing said parameter measurements in said blackout period;

calculating a model IC from said reprocessed parameter measurements;

calculating a plurality of single fault vectors;

calculating an estimate for each of said single fault vectors;

calculating a normalized error for each of said plurality of single fault vectors;

calculating a polarized error term for each of said plurality of single fault vectors; and

determining if double fault calculation is required;

calculating a plurality of double fault vectors;

calculating an estimate for each of said plurality of double fault vectors;

calculating a normalized error for each of said plurality of double fault vectors;

calculating a polarized error term for each of said plurality of double fault vectors;

comprising a smallest normalized error for each of said plurality of single fault vectors and a smallest normalized error for each of said plurality of double fault vectors.

10. The method of claim 8 wherein each of said plurality of parameter measurements comprises a differential of a current measurement and an immediately preceding measurement.

11. The method of claim 10 wherein each of said plurality of parameter measurement is a percentage differential.

12. The method of claim 8 wherein said performing single fault isolation comprises performing a statistical analysis.

13. The method of claim 8, wherein said performing single fault isolation comprises applying Kalman Filter parameter estimation.

14. The method of claim 8, wherein said plurality of parameter measurements are derived from an engine.

15. The method of claim 8, wherein said reprocessing said parameter measurements comprises the steps of:

computing a linear regression line from said plurality of parameter measurements comprising said latency period; and

recomputing each of said plurality of measurements comprising said latency period as a difference between said linear regression line and an average level prior to said event start.