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Research Papers: Gas Turbines: Aircraft Engine

Turbofan Engine Health Assessment From Flight Data

[+] Author and Article Information
N. Aretakis

Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: naret@central.ntua.gr

I. Roumeliotis

Section of Naval Architecture
and Marine Engineering,
Hellenic Naval Academy,
Piraeus 18539, Greece
e-mail: jroume@ltt.ntua.gr

A. Alexiou

Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: a.alexiou@ltt.ntua.gr

C. Romesis

Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: cristo@mail.ntua.gr

K. Mathioudakis

Professor
Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: kmathiou@central.ntua.gr

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 14, 2014; final manuscript received August 25, 2014; published online October 28, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(4), 041203 (Oct 28, 2014) (8 pages) Paper No: GTP-14-1388; doi: 10.1115/1.4028566 History: Received July 14, 2014; Revised August 25, 2014

The paper presents the use of different approaches to engine health assessment using on-wing data obtained over a year from an engine of a commercial short-range aircraft. The on-wing measurements are analyzed with three different approaches, two of which employ two models of different quality. Initially, the measurements are used as the sole source of information and are postprocessed utilizing a simple “model” (a table of corrected parameter values at different engine power levels) to obtain diagnostic information. Next, suitable engine models are built utilizing a semi-automated method which allows for quick and efficient creation of engine models adapted to specific data. Two engine models are created, one based on publicly available data and one adapted to engine specific on-wing “healthy” data. These models of different details are used in a specific diagnostic process employing model-based diagnostic methods, namely the probabilistic neural network (PNN) method and the deterioration tracking method. The results demonstrate the level of diagnostic information that can be obtained for this set of data from each approach (raw data, generic engine model or adapted to measurements engine model). A subsystem fault is correctly identified utilizing the diagnostic process combined with the engine specific model while the deterioration tracking method provides additional information about engine deterioration.

Copyright © 2015 by ASME
Topics: Engines , Flight
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References

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Figures

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Fig. 1

On-wing available measurements and station numbering

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Fig. 2

Raw and corrected EGT variation with engine flight cycles

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Fig. 3

EGTcor linear model extracted from the first 50 flight cycles

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Fig. 4

ΔEGT versus flight cycles

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Fig. 5

ΔWf versus flight cycles

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Fig. 6

ΔP3 versus flight cycles

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Fig. 7

ΔN2 versus flight cycles

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Fig. 8

ΔEGT versus flight cycles, T/O

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Fig. 9

Model creation process

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Fig. 10

Comparison between measurements and predictions of the ICAO and adapted engine models

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Fig. 11

Example of raw and preprocessed deltas

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Fig. 12

Considered PNN architecture

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Fig. 14

The deterioration tracking method results

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Fig. 15

ACC valve position recordings

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