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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Enhanced Fault Localization Using Probabilistic Fusion With Gas Path Analysis Algorithms

[+] Author and Article Information
A. Kyriazis

Laboratory of Thermal Turbomachines, National Technical University of Athens, P.O. Box 64069, Athens 15773, Greeceankyr@ltt.ntua.gr

K. Mathioudakis

Laboratory of Thermal Turbomachines, National Technical University of Athens, P.O. Box 64069, Athens 15773, Greecekmathiou@central.ntua.gr

J. Eng. Gas Turbines Power 131(5), 051601 (Jun 09, 2009) (9 pages) doi:10.1115/1.3078793 History: Received September 17, 2008; Revised September 30, 2008; Published June 09, 2009

A method for gas turbine fault identification from gas path data, in situations with a limited number of measurements, is presented. The method consists of a two stage process: (a) localization of the component or group of components with a fault and (b) fault identification by determining the precise location and magnitude of component performance deviations. The paper focuses on methods that allow improved localization of the faulty components. Gas path analysis (GPA) algorithms are applied to diagnostic sets comprising different combinations of engine components. The results are used to derive fault probabilities, which are then fused to derive a conclusion as to the location of a fault. Once the set of possible faulty components is determined, a well defined diagnostic problem is formulated and the faulty parameters are determined by means of a suitable algorithm. It is demonstrated that the method has an improved effectiveness when compared with previous GPA based methods.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Layout and station numbering of the considered engine

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Figure 2

Two modes of partitioning an engine: (a) Cold-hot-nozzle, (b) LP-HP-nozzle

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Figure 3

Derivation of fault probabilities by integration: (a) Nonfaulty health parameter, (b) faulty health parameter

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Figure 4

Flowchart of the procedure for faulty components selection

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Figure 5

Schematic representation of the whole procedure

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Figure 6

Probabilistic fusion for enhancement

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Figure 7

Probabilities after first pass from CHN and LHN partitions (FS)

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Figure 8

Diagnostic index after second pass, CHN, and LHN partitions (FS). (A8IMP parameter found faulty-NZLE component for CHN, SE2 parameter found faulty-high pressure compressor (HPC) component for LHN.)

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Figure 9

Probabilities after first pass from CHN and LHN partitions (CLT)

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Figure 10

Diagnostic Index after second pass, CHN, and LHN partitions (CLT). NZLE component found faulty by both CHN and LHN.

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Figure 11

Consensus probabilities after first passes of the utilized partitions (for FS and CLT)

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Figure 12

Diagnostic index after second pass from the utilized partitions. (A8IMP parameter found faulty-NZLE component.)

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