Degradation in the cooling effectiveness of a charge-air cooler (CAC) in a medium-duty turbocharged diesel engine has significant impact on engine performance. This degradation lowers the boost pressure and raises the intake manifold temperature. As a result, the engine provides lower horsepower and higher hydrocarbon levels than the rated values. The objective of this research is to monitor the health of the charge-air cooler by analyzing the intake manifold temperature signal. Experiments were performed on a Cummins ISB series turbocharged diesel engine, a 6-cylinder inline configuration with a 5.9 l displacement volume. Air flowing over the cooler was blocked by varying amounts, while various engine temperatures and pressures were monitored at different torque-speed conditions. Similarly, data were acquired without the introduction of any fault in the engine. For the construction of the manifold temperature trajectory vector, average mutual information estimates and a global false nearest neighbor analysis were used to find the optimal time parameter and embedding dimensions, respectively. The prediction of the healthy temperature vector was done by local linear regression using torque, speed, and their interaction as exogenous variables. Analysis of residuals generated by comparing the predicted healthy temperature vector and the observed temperature vector was successful in detecting the degradation of the charge-air cooler. This degradation was quantified by using box plots and probability density functions of residuals generated by comparing intake manifold temperature of healthy and faulty charge-air coolers. The general applicability of the model was demonstrated by successfully diagnosing a fault in the exhaust gas recirculation cooler of a different engine.

1.
Heywood
,
J. B.
, 1988,
Internal Combustion Engine Fundamentals
, 1st ed.,
McGraw-Hill
,
New York
.
2.
Bird
,
R. B.
,
Stewart
,
W. E.
, and
Lightfoot
,
E. N.
, 2006,
Transport Phenomena
, 2nd ed.,
Wiley
,
New York
.
3.
Jung
,
M.
,
Ford
,
R.
,
Glover
,
K.
,
Collings
,
N.
,
Christen
,
U.
, and
Watts
,
M.
, 2002, “
Parameterization and Transient Validation of a Variable Geometry Turbocharger for Mean-Value Modelling at Low and Medium Speed-Load Points
,” SAE Paper No. 2002-01-2729, pp.
1
14
.
4.
Rakopolous
,
C. D.
,
Michos
,
C. N.
, and
Giakoumis
,
E. G.
, 2005, “
Study of the Transient Behavior of Turbocharged Diesel Engines Including Compressor Surging Using Linearized Quasi-Steady Analysis
,” SAE Paper No. 2005-01-0225, pp.
37
52
.
5.
Moraal
,
P.
, and
Kolmanovsky
,
I. V.
, 1999, “
Turbocharger Modeling for Automotive Control Applications
,” SAE Paper No. 1999-01-0908, pp.
1
14
.
6.
Beard
,
R. A.
, and
Smith
,
G. J.
, 1971, “
A Method of Calculating the Heat Dissipation From Radiators to Cool Vehicle Engines
,” SAE Paper No. 710208, pp.
1
8
.
7.
Luptowski
,
B. J.
,
Arici
,
O.
,
Johnson
,
J. H.
, and
Parker
,
G. G.
, 2005, “
Development of Enhanced Vehicle and Engine Cooling System Simulation and Application to Active Cooling Control
,” SAE Paper No. 2005-01-0697, pp.
197
210
.
8.
Mladek
,
M.
, and
Guzzella
,
L.
, 2000, “
A Model for the Estimation of Inducted Air Mass and the Residual Gas Fraction Using Cylinder Pressure Measurements
,” SAE Paper No. 2000-01-0958, pp.
1
11
.
9.
Stefanopoulou
,
A. G.
,
Kolmanovsky
,
I. V.
, and
Freudenberg
,
J. S.
, 2000, “
Control of Variable Geometry Turbocharged Diesel Engines for Reduced Emissions
,”
IEEE Trans. Control Syst. Technol.
1063-6536,
8
(
4
), pp.
733
745
.
10.
Lauber
,
J.
, 2002, “
IC Engine: Tracking Control for an Inlet Manifold With EGR
,” SAE Paper No. 2002-01-2156, pp.
1
5
.
11.
He
,
Y.
,
Lin
,
C. C.
, and
Gangopadhyay
,
A.
, 2006, “
Integrated Simulation of the Engine and Control System of a Turbocharged Diesel Engine
,” SAE Paper No. 2006-01-0439, pp.
1
11
.
12.
Box
,
G. E. P.
,
Jenkins
,
G. M.
, and
Reinsel
,
G. C.
, 2004,
Time Series Analysis: Forecasting and Control
, 2nd ed.,
Pearson Education
,
Singapore
.
13.
Zhang
,
J. Q.
, and
Yan
,
Y.
, 2001, “
A Wavelet-Based Approach to Abrupt Fault Detection and Diagnosis of Sensors
,”
IEEE Trans. Instrum. Meas.
,
50
(
5
), pp.
1389
1396
. 0018-9456
14.
Bae
,
H.
,
Kim
,
Y. -T.
,
Kim
,
S.
,
Lee
,
S. -H.
, and
Wang
,
B. -H.
, 2004, “
Fault Detection of Induction Motors Using Fourier and Wavelet Analysis
,”
Journal of Advanced Computational Intelligence and Intelligent Informatics
,
8
(
4
), pp.
431
436
.
15.
Mees
,
A. I.
, 2001,
Nonlinear Dynamics and Statistics
, 1st ed.,
Birkhauser
,
Boston
.
16.
Kantz
,
H.
, and
Schreiber
,
T.
, 2002,
Nonlinear Time Series Analysis
, 1st ed.,
Cambridge University
,
Cambridge, England
.
17.
Chan
,
K. -S.
, and
Tong
,
H.
, 2001,
Chaos: A Statistical Perspective
(
Springer Series in Statistics
),
Springer-Verlag
,
New York
.
19.
Abarbanel
,
H. D. I.
, 2001, “
Challenges in Modeling Nonlinear Systems: A Worked Example
,”
Nonlinear Dynamics and Statistics
,
1
(
1
), pp.
1
30
.
20.
Judd
,
K.
,
Small
,
M.
, and
Mees
,
A. I.
, 2001, “
Achieving Good Nonlinear Models: Keep It Simple, Vary the Embedding, and Get the Dynamics Right
,”
Nonlinear Dynamics and Statistics
,
1
(
3
), pp.
65
80
.
21.
Stark
,
J.
, 2001, “
Delay Reconstruction: Dynamics Versus Statistics
,”
Nonlinear Dynamics and Statistics
,
1
(
4
), pp.
81
104
.
22.
Cover
,
T.
, and
Thomas
,
J.
, 1993,
Elements of Information Theory
,
Wiley
,
New York
.
23.
Scott
,
D. W.
, 1992,
Multivariate Density Estimation: Theory, Practice, and Visualization
(
Wiley Series in Probability and Statistics
),
Wiley
,
London
.
24.
Battiti
,
R.
, 1994, “
Using Mutual Information for Selecting Features in Supervised Neural Net Learning
,”
IEEE Trans. Neural Netw.
1045-9227,
5
(
4
), pp.
537
550
.
25.
Kennel
,
M. B.
,
Brown
,
R.
, and
Abarbanel
,
H.
, 1992, “
Determining Embedding Dimension for Phase-Space Reconstruction Using a Geometrical Construction
,”
Phys. Rev. A
1050-2947,
45
(
6
), pp.
3403
3411
.
26.
Abarbanel
,
H.
, and
Kennel
,
M. B.
, 1993, “
Local False Neighbors and Dynamical Dimensions From Observed Chaotic Data
,”
Phys. Rev. E
1063-651X,
47
(
5
), pp.
3057
3068
.
27.
Abarbanel
,
H.
,
Katz
,
R. A.
,
Galib
,
T.
,
Cembrola
,
J.
, and
Frison
,
T. W.
, 1994, “
High Reynolds Number Boundary Layer Chaos
,”
Phys. Rev. Lett.
0031-9007,
72
(
15
), pp.
2383
2386
.
28.
Kennel
,
M. B.
, and
Abarbanel
,
H.
, 2002, “
False Neighbors and False Strands: A Reliable Minimum Embedding Dimension Algorithm
,”
Phys. Rev. E
1063-651X,
66
(
2
), p.
026209
.
29.
Cleveland
,
W. S.
, and
Devlin
,
S. J.
, 1988, “
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
,”
J. Am. Stat. Assoc.
0162-1459,
83
, pp.
596
610
.
30.
Tukey
,
J. W.
, 1977,
Exploratory Data Analysis
, 1st ed.,
Addison-Wesley
,
Reading, MA
.
31.
Joshi
,
A. A.
, 2007. “
Strategies for Data-Based Diesel Engine Fault Diagnostics
,” Ph.D. thesis, Purdue University, West Lafayette, IN.
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