Module performance analysis is a well-established framework to assess changes in the health condition of the components of the engine gas-path. The primary material of the technique is the so-called vector of residuals, which are built as the difference between actual measurement taken in the gas-path and the values predicted by means of an engine model. Obviously, the quality of the assessment of the engine condition depends strongly on the accuracy of the engine model. The present paper proposes a new approach for data-driven modeling of a fleet of engines of a given type. Such black-box models can be designed by operators, such as airlines and third-party companies. The fleet-wide modeling process is formulated as a regression problem that provides a dedicated model for each engine in the fleet, while recognizing that all engines are of the same type. The methodology is applied to a virtual fleet of engines generated within the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) environment. The set of models is assessed quantitatively through the coefficient of determination and is further used to perform anomaly detection.

References

References
1.
Rajamani
,
R.
,
Wang
,
J.
, and
Jeong
,
K. Y.
,
2004
, “
Condition-Based Maintenance for Aircraft Engines
,”
ASME
Paper No. GT2004-54127.
2.
Volponi
,
A. J.
,
2003
, “
Foundation of Gas Path Analysis (Part I and II)
,”
Gas Turbine Condition Monitoring and Fault Diagnosis
(VKI Lecture Series, Vol. 1),
von Karman Institute
,
Brussels, Belgium
.
3.
Mattingly
,
J. D.
,
1996
,
Elements of Gas Turbine Propulsion
,
McGraw-Hill
,
New York
.
4.
Walsh
,
P. P.
, and
Fletcher
,
P.
,
1998
,
Gas Turbine Performance
,
Blackwell Science
,
New York
.
5.
Kurzke
,
J.
,
2013
, gasturb
12 User's Manual
,
GasTurb GmbH
,
Dachau, Germany
.
6.
Visser
,
W.
, and
Broomhead
,
M.
,
2000
, “
gsp: A Generic Object-Oriented Gas Turbine Simulation Environment
,”
ASME
Paper No. 2000-GT-0002.
7.
May
,
R.
,
Csank
,
J.
,
Lavelle
,
T.
,
Litt
,
J.
, and
Guo
,
T.-H.
,
2010
, “
A High Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller
,”
AIAA
Paper No. 2010-6630.
8.
Verbist
,
M.
,
Visser
,
W.
, and
van Buijtenen
,
J. P.
,
2013
, “
Experience With Gas-Path Analysis for On-Wing Turbofan Condition Monitoring
,”
ASME
Paper No. GT2013-95739.
9.
Aretakis
,
N.
,
Roumeliotis
,
I.
,
Alexiou
,
A.
,
Romessis
,
C.
, and
Mathioudakis
,
K.
,
2014
, “
Turbofan Engine Health Assessment From Flight Data
,”
ASME
Paper No. GT2014-26443.
10.
Simon
,
D. L.
, and
Rinehart
,
A. W.
,
2014
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data
,”
ASME
Paper No. GT2014-27172.
11.
Dewallef
,
P.
, and
Léonard
,
O.
,
2001
, “
On-Line Validation of Measurements on Jet Engines Using Automatic Learning Methods
,” 15th International Symposium on Air Breathing Engines, Bangalore, Sept. 2–7, Paper No. ISABE-2001-1031.
12.
Chiras
,
N.
,
Evans
,
C.
, and
Rees
,
D.
,
2002
, “
Nonlinear Gas Turbine Modelling Using Feedforward Neural Networks
,”
ASME
Paper No. GT2002-30035.
13.
Herzog
,
J.
,
Hanlin
,
J.
,
Wegerich
,
S.
, and
Wilks
,
A.
,
2005
, “
High Performance Condition Monitoring of Aircraft Engines
,”
ASME
Paper No. GT2005-68485.
14.
Loboda
,
I.
,
Yepifanov
,
S.
, and
Feldshteyn
,
Y.
,
2009
, “
Analysis of Maintenance Data of a Gas Turbine for Driving an Electric Generator
,”
ASME
Paper No. GT2009-60176.
15.
Lacaille
,
J.
, and
Côme
,
E.
,
2011
, “
Visual Mining and Statistics for a Turbofan Engine Fleet
,”
IEEE Aerospace Conference
,
Big Sky, MT
, Mar. 5–12.
16.
Loboda
,
I.
, and
Feldshteyn
,
Y.
,
2010
, “
Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study
,”
ASME
Paper No. GT2010-23749.
17.
Côme
,
E.
,
Cottrell
,
M.
,
Verleysen
,
M.
, and
Lacaille
,
J.
,
2011
, “
Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance
,”
8th International Conference on Advances in Self-Organizing Maps (WSOM’11), Espoo
, Finland, June 13–15,
Springer-Verlag
,
New York
, pp.
298
307
.
18.
Chu
,
E.
,
Gorinevsky
,
D.
, and
Boyd
,
S.
,
2010
, “
Detecting Aircraft Performance Anomalies From Cruise Flight Data
,”
AIAA
Paper No. 2010-3307.
19.
Chu
,
E.
,
Gorinevsky
,
D.
, and
Boyd
,
S.
,
2011
, “
Scalable Statistical Monitoring of Fleet Data
,”
18th World IFAC Congress
,
Milan, Italy
, Aug. 28–Sept. 2, pp.
13227
13232
.
20.
Simon
,
D. L.
,
2010
, “
Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User's Guide
,” NASA Glenn Research Center, Cleveland, OH, Technical Memorandum No. TM-2010-215840.
21.
Uday
,
P.
, and
Ganguli
,
R.
,
2010
, “
Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filter
,”
ASME J. Eng. Gas Turbines Power
,
132
(
4
), p.
041601
.
22.
Simon
,
D. L.
,
Borguet
,
S.
,
Léonard
,
O.
, and
Zhang
,
X.
,
2014
, “
Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results
,”
ASME J. Eng. Gas Turbines Power
,
136
(
4
), p.
041201
.
23.
Dewallef
,
P.
, and
Léonard
,
O.
,
2001
, “
Robust Measurement Validation on Jet-Engines
,”
4th European Conference on Turbomachinery
,
Florence
, Mar. 20–23, pp.
899
909
.
24.
Borguet
,
S.
, and
Léonard
,
O.
,
2008
, “
A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach
,”
ASME J. Eng. Gas Turbines Power
,
130
(
2
), p.
021605
.
You do not currently have access to this content.