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.

References

References
1.
Jaw
,
L. C.
,
2005
, “
Recent Advances in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step
,”
ASME
Paper No. GT2005-68625.10.1115/GT2005-68625
2.
The Technical Cooperation Program (TTCP) Website, accessed January 9,
2013
, http://www.acq.osd.mil/ttcp/
3.
Simon
,
D. L.
,
2010
, “
Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User's Guide
,” NASA/TM-2010-215840.
4.
Volponi, A. J., 2003, “Foundations of Gas Path Analysis (Part I and II),” Gas Turbine Condition Monitoring and Fault Diagnosis (von Karman Institute Lecture Series No. 2003-01), von Karman Institute, Rhode-Saint-Genèse, Belgium.
5.
Volponi
,
A. J.
,
1999
, “
Gas Turbine Parameter Corrections
,”
ASME J. Eng. Gas Turb. Power
,
121
, pp.
613
621
.10.1115/1.2818516
6.
DePold
,
H. R.
, and
Gass
,
F. D.
,
1999
, “
The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics
,”
ASME J. Eng. Gas Turb. Power
,
121
, pp.
607
612
.10.1115/1.2818515
7.
Demuth
,
H.
, and
Beale
,
M.
,
1992
, “
Neural Network Toolbox for Use With MATLAB
.” Version 4, MathWorks, Natick, MA, Chap. 7.
8.
Kobayashi
,
T.
,
Simon
,
D. L.
, and
Litt
,
J. S.
,
2005
, “
Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters
,”
ASME
Paper No. GT2005-68494.10.1115/GT2005-68494
9.
Borguet
,
S.
, and
Léonard
,
O.
,
2009
, “
A Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring
,”
ASME J. Eng. Gas Turb. Power
,
131
, p.
011601
.10.1115/1.2967493
10.
Doel
,
D. L.
,
1994
, “
An Assessment of Weighted-Least-Squares-Based Gas Path Analysis
,”
ASME J. Eng. Gas Turb. Power
,
116
, pp.
366
373
.10.1115/1.2906829
11.
Borguet
,
S.
, and
Léonard
,
O.
,
2010
, “
A Sparse Estimation Approach to Fault Isolation
,”
ASME J. Eng. Gas Turb. Power
,
132
, p.
021601
.10.1115/1.3156815
12.
Borguet
,
S.
, and
Léonard
,
O.
,
2011
, “
Constrained Sparse Estimation for Improved Fault Isolation
,”
ASME J. Eng. Gas Turb. Power
,
133
, p.
121602
.10.1115/1.4004013
13.
Ioannou
,
P. A.
, and
Sun
,
J.
,
1996
,
Robust Adaptive Control
,
Prentice-Hall
,
Englewood Cliffs, NJ
.
14.
Chen
,
J.
, and
Patton
,
R. J.
,
1999
,
Robust Model-Based Fault Diagnosis for Dynamic Systems
,
Kluwer Academic Publishers
,
London
.
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