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Research Papers

Evaluation of Aircraft Engine Gas Path Diagnostic Methods Through ProDiMES

[+] Author and Article Information
Anastasios O. Koskoletos

Laboratory of Thermal Turbomachines,
National Technical University,
Athens 15710, Greece
e-mail: orestiskosko92@mail.ntua.gr

Nikolaos Aretakis

Assistant Professor
Laboratory of Thermal Turbomachines,
National Technical University,
Athens 15710, Greece
e-mail: naret@central.ntua.gr

Alexios Alexiou

Laboratory of Thermal Turbomachines,
National Technical University,
Athens 15710, Greece
e-mail: a.alexiou@ltt.ntua.gr

Christoforos Romesis

Laboratory of Thermal Turbomachines,
National Technical University,
Athens 15710, Greece
e-mail: cristo@mail.ntua.gr

Konstantinos Mathioudakis

Professor
Laboratory of Thermal Turbomachines,
National Technical University,
Athens 15710, Greece
e-mail: kmathiou@central.ntua.gr

Manuscript received June 22, 2018; final manuscript received July 5, 2018; published online November 29, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 140(12), 121016 (Nov 29, 2018) (12 pages) Paper No: GTP-18-1288; doi: 10.1115/1.4040909 History: Received June 22, 2018; Revised July 05, 2018

Propulsion diagnostic method evaluation strategy (ProDiMES) offers an aircraft engine diagnostic benchmark problem where the performance of candidate diagnostic methods is evaluated while a fair comparison can be established. In the present paper, the performance evaluation of a number of gas turbine diagnostic methods using the ProDiMES software is presented. All diagnostic methods presented here were developed at the Laboratory of Thermal Turbomachinery of the National Technical University of Athens (LTT/NTUA). Component, sensor, and actuator fault scenarios that occur in a fleet of deteriorated twin-spool turbofan engines are considered. The performance of each diagnostic method is presented through the evaluation metrics introduced in the ProDiMES software. Remarks about each methods performance as well as the detectability and classification rates of each fault scenario are made.

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References

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Aretakis, N. , Mathioudakis, K. , and Stamatis, A. , 2003, “ Non-Linear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach,” ASME J. Eng. Gas Turbines Power, 125(3), pp. 642–650. [CrossRef]
Stamatis, A. , Mathioudakis, K. , and Papailiou, K. , 1992, “ Optimal Measurements and Health Indices Selection for Gas Turbine Performance Status and Fault Diagnosis,” ASME J. Eng. Gas Turbines Power, 114(2), pp. 209–216. [CrossRef]

Figures

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Fig. 1

Diagnostic framework flow chart

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Fig. 2

Fault detection step flow chart

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Fig. 3

Method 5 flow chart

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Fig. 4

Method 6 flow chart

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Fig. 5

Detailed kappa coefficient (component fault test cases)

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Fig. 6

Detailed CCR (component fault test cases)

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Fig. 7

Detailed CCR (all fault test cases)

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Fig. 8

Detailed CCR (blind test cases)

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Fig. 9

Hardly detectable fault scenarios

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Fig. 10

Fan–Nf and LPC–VBV normalized mean signatures

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Fig. 11

C-MAPSS-SS engine layout

Tables

Errata

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