Process and methodology for root cause identification in gas turbine engine performance tracking

• US 20080221835A1
• Filed: 03/05/2007
• Published: 09/11/2008
• Est. Priority Date: 03/05/2007
• 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 7,788,014 B2
• Time in Patent Office: Days
• US Class Current: 702/185
• CPC Class Codes: F05B 2260/80 Diagnostics
• CPC Class Codes: G05B 23/0278 Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA

Abstract

A system and method for narrowing an assessment of root cause to a single stage within a gas turbine engine module using coupling factors and known instrumentation non-repeatability. Embodiments examine certain root cause (RC) effects that manifest themselves as coupling levels, or ratios (CR), between an engine module'"'"'s efficiency change Δη and its flow parameter change ΔFP.

Claims

1. A method for narrowing an assessment of root cause RC to a single turbine or compressor stage, or a combination of stages within a gas turbine engine module comprising:

inputting engine gas path measurement data;

performing a gas path analysis on the gas path measurement data to derive % Δ

performance vectors;

calculating estimated module efficiency {circumflex over (η

)} and flow parameter F{circumflex over (P)} deltas (Δ

'"'"'s) for each % Δ

performance vector;

calculating a coupling ratio Δ

C{circumflex over (R)} for each % Δ

performance vector;

calculating a variability for each coupling ratio Δ

C{circumflex over (R)};

calculating a level-of-belief lob(x,RC_i) for each coupling ratio Δ

C{circumflex over (R)} and each root cause RC_i;

calculating a confidence level for each module RC_k; and

selecting a module root cause RC having a maximum confidence level.

2. The method according to claim 1 wherein the variability further comprises calculating a mean Z, a variance σ

²(i) and covariance σ

_{C{circumflex over (R)}} for each % Δ

performance vector.

3. The method according to claim 2 wherein calculating estimated module efficiency {circumflex over (η

)} and flow parameter F{circumflex over (P)} deltas (Δ

'"'"'s) for each % Δ

performance vector is performed using a Kalman filter.

4. The method according to claim 2 wherein the estimated module efficiency {circumflex over (η

)} and flow parameter F{circumflex over (P)} deltas (Δ

'"'"'s) are used to determine and estimate the coupling ratios C{circumflex over (R)} for each engine module.

5. The method according to claim 4 wherein the coupling ratio CR is a linear dependency between the estimated module efficiency {circumflex over (η

)} and flow parameter F{circumflex over (P)} deltas (Δ

'"'"'s).

6. The method according to claim 5 wherein the variability for each coupling ratio Δ

C{circumflex over (R)} is calculated empirically and represented by a measurement covariance matrix R.

7. The method according to claim 6 wherein the covariance σ

_{C{circumflex over (R)}} is obtained empirically through simulation by generating N noisy measurement vectors and calculating the associated N performance estimates.