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

Optimization of Aero Gas Turbine Maintenance Using Advanced Simulation and Diagnostic Methods

[+] Author and Article Information
Joern Kraft

Engine Overhaul Department,
Lufthansa Technik AG,
Weg beim Jäger 193,
Hamburg 22335, Germany
e-mail: Joern.Kraft@lht.dlh.de

Vishal Sethi

Department of Power and Propulsion,
Cranfield University,
Cranfield, Bedfordshire MK43 0AL, UK
e-mail: V.Sethi@cranfield.ac.uk

Riti Singh

Department of Power and Propulsion,
Cranfield University,
Cranfield, Bedfordshire MK43 0AL, UK
e-mail: R.Singh@cranfield.ac.uk

Contributed by the Controls, Diagnostics and Instrumentation Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received May 7, 2013; final manuscript received March 31, 2014; published online May 16, 2014. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 136(11), 111601 (May 16, 2014) (10 pages) Paper No: GTP-13-1134; doi: 10.1115/1.4027356 History: Received May 07, 2013; Revised March 31, 2014

Engine maintenance costs are a major contributor to the direct operating costs of aircraft. Therefore, the minimization of engine maintenance costs per flight-hour is a key aspect for airlines to operate successfully under challenging market conditions. Minimization can be achieved by increasing the on-wing time or by reducing the shop-visit costs. Combining both provides optimum results and can only be achieved by thorough understanding of the engine. In the past, maintenance optimization was mainly an experience-based process. In this work, a novel analytical approach is presented to optimize the maintenance of commercial turbofan engines. A real engine fleet of more than 100 long-haul engines is used to demonstrate the application. The combination of advanced diagnostic and simulation methods with classical hardware-based failure analysis enables linking of overall engine performance with detailed hardware condition and, thus, an effective optimization of the overall maintenance process.

Copyright © 2014 by ASME
Topics: Maintenance , Engines
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Fig. 1

Thermodynamic zooming model of the engine

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Fig. 2

Evaluations of parts-performance contribution

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Fig. 3

HPC modifier measured on engines A to F plotted in fault-influence-diagram

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Fig. 4

HPC blade tip-rub (curling): mid stages of engine F

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Fig. 5

HPC blade leading edge blunting: forward stages, engine C, left: overview, right-upper: LE cross section, right-lower: micrograph LE

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Fig. 6

Corrected δ engine EGT versus test-cell LPT exit temperature T55

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Fig. 7

Analysis results of engine A analysis run 1 and run 2

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Fig. 8

Process of work-scope prediction and selection

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Fig. 10

Predicted (minimum and average performance restoration) and actual effect of work scope on module parameters

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Fig. 11

Comparison of analysis results for run 2 and run 3

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Fig. 9

Predicted (minimum and average performance restoration) and actual effect of work scope on engine main parameters

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Fig. 12

Improved predicted and actual effect of work scope on engine main parameters (original prediction for comparison)

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Fig. 13

Improved predicted and actual effect of work scope on module parameters (original prediction for comparison)

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Fig. 14

Comparison of EGT-margin pre and post improvements initiated by the project

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Fig. 15

On-wing EGT-margin trend for pre and post new maintenance concept engines




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