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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics & Instrumentation

Bayesian Network Approach for Gas Path Fault Diagnosis

[+] Author and Article Information
C. Romessis

Laboratory of Thermal Turbomachines, National Technical University of Athens, P. O. Box 64069, Athens 15710, Greececristo@mail.ntua.gr

K. Mathioudakis

Laboratory of Thermal Turbomachines, National Technical University of Athens, P. O. Box 64069, Athens 15710, Greecekmathiou@central.ntua.gr

J. Eng. Gas Turbines Power 128(1), 64-72 (Mar 01, 2004) (9 pages) doi:10.1115/1.1924536 History: Received October 01, 2003; Revised March 01, 2004

A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.

Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flowchart of the overall procedure of the proposed diagnostic method

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Figure 2

The architecture of a diagnostic BBN

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Figure 3

Schematic presentation of the states of a health parameter node of the diagnostic BBN

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Figure 4

Schematic presentation of the states of a measurement node of the diagnostic BBN

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Figure 5

Schematic presentation of the procedure for specifying the CPTs of the diagnostic BBN

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Figure 6

The architecture of the diagnostic BBN for the considered turbofan case

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Figure 7

Schematic presentation of the states of the flow and efficiency factor nodes of the diagnostic BBN

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Figure 8

Schematic presentation of the a priori probabilities given at a flow factor node of the diagnostic BBN

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Figure 9

(a) Example of a FD diagnosis ( examined fault case: ΔSE26=−1%). (b) Example of a UD diagnosis (examined fault case: ΔSE49=−0.6%)

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Figure 10

Effect of noise on the diagnostic ability of the proposed method

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Figure 11

Diagnostic performance of the method over a flight

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Figure 12

Diagnostic performance of the method over a flight using two BBNs

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Figure 13

Layout and station numbering and measurements of the considered turbofan engine

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Figure 14

Architecture of an example BBN

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Figure 15

Simulation of data of fault cases with the aid of EPM

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Figure 16

The sections of a flight and the considered operating points

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