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Research Papers: Gas Turbines: Aircraft Engine

Experience With Gas Path Analysis for On-Wing Turbofan Condition Monitoring

[+] Author and Article Information
Michel L. Verbist

Faculty of Mechanical,
Maritime and Materials Engineering,
Delft University of Technology, Delft, Netherlands
e-mail: mlverbist@gmail.com

Wilfried P. J. Visser

Delft University of Technology,
Delft, Netherlands
e-mail: wvisser@xs4all.nl

Jos P. van Buijtenen

Faculty of Aerospace Engineering,
Delft University of Technology, Delft, Netherlands

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 1, 2013; final manuscript received July 31, 2013; published online October 25, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(1), 011204 (Oct 25, 2013) (8 pages) Paper No: GTP-13-1212; doi: 10.1115/1.4025347 History: Received July 01, 2013; Revised July 31, 2013

Gas path analysis (GPA) is an effective method for determination of turbofan component condition from measured performance parameters. GPA is widely applied on engine test rig data to isolate components responsible for performance problems, thereby offering substantial cost saving potential. Additional benefits can be obtained from the application of GPA to on-wing engine data. This paper describes the experience with model-based GPA on large volumes of on-wing measured performance data. Critical is the minimization of the GPA results uncertainty in order to maintain reliable diagnostics and condition monitoring information. This is especially challenging given the variable in-flight operating conditions and limited on-wing sensor accuracy. The uncertainty effects can be mitigated by statistical analysis and filtering and postprocessing of the large datasets. By analyzing correlations between measured performance data trends and estimated component condition trends errors can be isolated from the GPA results. The various methods assessed are described and results are demonstrated in a number of case studies on a large turbofan engine fleet.

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References

Visser, W. P. J., and Broomhead, M. J., 2000, “GSP, a Generic Object-Oriented Gas Turbine Simulation Environment,” ASME Turbo Expo, Munich, Germany, May 8–11.
Visser, W. P. J., Kogenhop, O., and Oosteveen, M., 2006, “A Generic Approach for Gas Turbine Adaptive Modeling,” ASME J. Eng. Gas. Turb. Power, 128(1), pp. 13–19. [CrossRef]
Visser, W. P. J., Oostveen, M., and Pieters, H.v.D., 2006, “Experience With GSP as a Gas Path Analysis Tool,” ASME Turbo Expo, Barcelona, Spain, May 8–11, ASME Paper No. GT2006-90904. [CrossRef]
Lambiris, B., 1994, “Adaptive Modeling of Jet Engine Performance With Application to Condition Monitoring,” J. Propul. Power, 10(6), pp. 890–896. [CrossRef]
Li, Y. G., Pilidis, P., and Newby, M. A., 2005, “An Adaptation Approach for Gas Turbine Design-Point Performance Simulation,” ASME Turbo Expo, Reno–Tahoe, NV, June 6–9, ASME Paper No. GT2005-68140. [CrossRef]
Stamatis, A., Mathioudakis, K., and Papailiou, K. D., 1990, “Adaptive Simulation of Gas Turbine Performance,” ASME J. Eng. Gas. Turb. Power, 112, pp. 168–175. [CrossRef]
Verbist, M., 2011, “Model-Based Gas Turbine Diagnostics at KLM Engine Services,” 20th ISABE Conference, Gotenburg, Sweden, September 12–16.
Mathioudakis, K., and Tslavoutas, A., 2001, “Uncertainty Reduction in Gas Turbine Performance Diagnostics by Accounting for Humidity Effects,” ASME Turbo Expo, New Orleans, LA, June 4–7.
Taylor, J. R., 1996, An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books, Sausalito, CA.

Figures

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Fig. 1

Sensor locations in the GE CF6-80C2 turbofan engine

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Fig. 2

Effects of increasing customer bleed flow on GPA results when these are not taken into account in the model

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Fig. 3

HPT efficiency condition deviations obtained from GPA with on-wing data for 100 consecutive takeoffs

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Fig. 4

HPT mass flow capacity condition deviations obtained from GPA with on-wing data for 100 consecutive takeoffs

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Fig. 5

Probability density function of the residual errors between the smoothed and observed value

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Fig. 6

Hot day exhaust gas temperature margin trend from approximately 600 consecutive takeoff snapshots

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Fig. 7

HPC efficiency deviation trend. An exponential weighted moving average (EWMA) is used to represent a smoothed trend.

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Fig. 8

High pressure turbine efficiency deviation trend

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Fig. 9

Low pressure turbine flow capacity deviation trend

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Fig. 10

Hot day exhaust gas temperature margin trend from approximately 500 consecutive takeoff snapshots. The arrows indicate the observed EGT hot day margin shifts.

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Fig. 11

Corrected Tt25 temperature trend

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Fig. 12

Corrected Tt45 temperature trend

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Fig. 13

Corrected Tt5 temperature trend

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Fig. 14

Corrected Pt25 pressure trend

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Fig. 15

Corrected Ps3 pressure trend

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Fig. 16

Corrected Pt45 pressure trend

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Fig. 17

(a) Corrected shaft speed trends for the N1 shaft and (b) the N2 shaft

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Fig. 18

This figure shows the original Pt25 parameter data, including the downward shift, and the data corrected with the offset value

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Fig. 19

(a) Estimated efficiency deviation and (b) flow capacity deviation for the fan bypass

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Fig. 20

(a) Estimated efficiency deviation and (b) flow capacity deviation for the combined fan core and booster

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Fig. 21

(a) Estimated efficiency deviation and (b) flow capacity deviation for the high pressure compressor

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Fig. 22

(a) Estimated efficiency deviation and (b) flow capacity deviation for the high pressure turbine

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Fig. 23

(a) Estimated efficiency deviation and (b) flow capacity deviation for the low pressure turbine

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