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Research Papers: Gas Turbines: Aircraft Engine

An Approach for Optimal Measurements Selection on Gas Turbine Engine Fault Diagnosis

[+] Author and Article Information
Min Chen

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: chenmin@buaa.edu.cn

Liang Quan Hu

Collaborative Innovation Center of Advanced
Aero-Engine,
Beijing 100191, China
e-mail: lovehuliangquan@163.com

Hailong Tang

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: 75249612@qq.com

1Corresponding author.

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 1, 2014; final manuscript received November 8, 2014; published online December 23, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(7), 071203 (Jul 01, 2015) (9 pages) Paper No: GTP-14-1523; doi: 10.1115/1.4029171 History: Received September 01, 2014; Revised November 08, 2014; Online December 23, 2014

Gas path fault diagnosis plays an important role in guaranteeing safe, reliable and cost-effective operation for gas turbine engines. Measurements selection is among the most critical issues for diagnostic method implementation. In this paper, an integration approach for optimal measurements selection, which combines finger print diagrams analysis, health parameters correlation analysis, performance estimation uncertainty index analysis and fault cases validation based on genetic algorithm, has been proposed and applied to assess the health condition of a two-spool split flow turbofan in test bed. First, mathematical description of an engine gas path fault diagnosis process was given and the influence coefficient matrix was also calculated based on a well calibrated nonlinear engine performance simulation model. Second, the number of combination candidates was reduced from 782 to 256 and three measurements were picked out using the finger print diagrams analysis and the health parameters correlation analysis. Then, the number of the combination candidates was further narrowed down to 13 using the performance estimation uncertainty index analysis. A nonlinear genetic algorithm fault diagnosis method was applied to test the diagnostic ability of the remaining measurement candidates. Finally, an optimal measurement combination was worked out which demonstrated the effectiveness of the integration approach. This integration approach for optimal measurements selection is also applicable to other type of gas turbine engines.

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Figures

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Fig. 1

Two-spool split flow turbofan structure diagram

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Fig. 2

Flow chart of measurements selection process

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Fig. 3

Total air flow rate (WA) finger print diagram

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Fig. 4

Correlation analysis diagram between E1 and E4

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Fig. 5

Correlation analysis diagram between E2 and E3

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Fig. 6

Measurement combinations validation using genetic algorithm

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Fig. 7

Single component fault identification results

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Fig. 8

Two components faults identification results

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Fig. 9

Three components faults identification results

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Fig. 10

Four components faults identification results

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