Research Papers: Gas Turbines: Aircraft Engine

Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

[+] Author and Article Information
Donald L. Simon

Controls and Dynamics Branch,
NASA Glenn Research Center,
21000 Brookpark Road, MS 77-1,
Cleveland, OH 44135

Olivier Léonard

Turbomachinery Group,
University of Liège,
Campus du Sart-Tilman,
Liège B52/3 4000, Belgium

Xiaodong (Frank) Zhang

Department of Electrical Engineering,
335 Russ Engineering Center,
3640 Colonel Glenn Highway,
Wright State University,
Dayton, OH 45435

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 9, 2013; final manuscript received September 3, 2013; published online December 10, 2013. Editor: David Wisler.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Eng. Gas Turbines Power 136(4), 041201 (Dec 10, 2013) (10 pages) Paper No: GTP-13-1299; doi: 10.1115/1.4025482 History: Received August 09, 2013; Revised September 03, 2013

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate, and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

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Fig. 2

Diagnostic process applied for diagnostic methods #1 and #2

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Fig. 1

ProDiMES public benchmarking process

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Fig. 3

Block diagram showing the main components of diagnostic method #3

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Fig. 4

Sliding window for fault detection

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Fig. 5

Comparison of the penalization induced by the quadratic (gray) and absolute value (black) regularization terms

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Fig. 6

Diagnostic method #4 fault detection and isolation architecture

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Fig. 8

True positive rate

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Fig. 7

Structure of FIE for Nf sensor fault

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Fig. 10

Kappa coefficient

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Fig. 11

Correct classification rate




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