A diesel engine electrical generator set (“gen-set”) was instrumented with in-cylinder indicating sensors as well as acoustic emission microphones near the engine. Air filter clogging was emulated by progressive restriction of the engine's inlet air flow path during which comprehensive engine and acoustic data were collected. Fast Fourier transforms (FFTs) were analyzed on the acoustic data. Dominant FFT peaks were then applied to supervised machine learning neural network analysis with matlab-based tools. The progressive detection of the air path clogging was audibly determined with correlation coefficients greater than 95% on test data sets for various FFT minimum intensity thresholds. Further, unsupervised machine learning self-organizing maps (SOMs) were produced during normal-baseline operation of the engine. The degrading air flow engine sound data were then applied to the normal-baseline operation SOM. The quantization error (QE) of the degraded engine data showed clear statistical differentiation from the normal operation data map. This unsupervised SOM-based approach does not know the engine degradation behavior in advance, yet shows clear promise as a method to monitor and detect changing engine operation. Companion in-cylinder combustion data additionally shows the degrading nature of the engine's combustion with progressive airflow restriction (richer and lower density combustion).

References

References
1.
Wong
,
K. I.
,
Wong
,
P. K.
,
Cheung
,
C. S.
, and
Vong
,
C. M.
,
2013
, “
Modeling and Optimization of Biodiesel Engine Performance Using Advanced Machine Learning Methods
,”
Energy
,
55
(
15
), pp.
519
528
.
2.
Albarbar
,
A.
,
Gu
,
F.
, and
Ball
,
A. D.
,
2010
, “
Diesel Engine Fuel Injection Monitoring Using Acoustic Measurements and Independent Component Analysis
,”
Measurement
,
43
(
10
), pp.
1376
1386
.
3.
Sharkey
,
A. J. C.
,
Chandroth
,
G. O.
, and
Sharkey
,
N. E.
,
2000
, “
A Multi-Net System for the Fault Diagnosis of a Diesel Engine
,”
Neural Comput. Appl.
,
9
(
2
), pp.
152
160
.
4.
Fischer
,
M.
,
Boettcher
,
J.
,
Kirkham
,
C.
, and
Georgi
,
R.
,
2009
, “
OBD of Diesel EGR Using Artificial Neural Networks
,”
SAE
Paper No. 2009-01-1427.
5.
Ghaboussi
,
J.
, and
Banan
,
M. R.
,
1994
, “
Neural Networks in Engineering Diagnostics
,”
SAE
Paper No. 941116.
6.
Huang
,
R.
,
Xi
,
L.
,
Li
,
X.
,
Richard Liu
,
C.
,
Qiu
,
H.
, and
Lee
,
J.
,
2007
, “
Residual Life Predictions for Ball Bearings Based on Self-Organizing Map and Back Propagation Neural Network Methods
,”
Mech. Syst. Signal Process.
,
21
(
1
), pp.
193
207
.
7.
Kohonen
,
T.
,
Oja
,
E.
,
Simula
,
O.
,
Visa
,
A.
, and
Kangas
,
J.
,
1996
, “
Engineering Applications of the Self-Organizing Map
,”
Proc. IEEE
,
84
(
10
), pp.
1358
1384
.
8.
Alhoniemi
,
E.
,
Hollmen
,
J.
,
Simula
,
O.
, and
Vesanto
,
J.
,
1999
, “
Process Monitoring and Modeling Using the Self-Organizing Map
,”
Integr. Comput.-Aided Eng.
,
6
(
1
), pp.
3
14
.
9.
Ypma
,
A.
, and
Duin
,
R. P. W.
,
1997
, “
Novelty Detection Using Self-Organizing Maps
,”Progress in Connectionist-Based Information Systems, Vol. 2, Springer, New York, pp.
1322
1325
.
10.
Ball
,
A. D.
,
Gu
,
F.
, and
Li
,
W.
,
2000
, “
The Condition Monitoring of Diesel Engines Using Acoustic Measurements—Part 2: Fault Detection and Diagnosis
,”
SAE
Paper No. 2000-01-0368.
11.
Luning Prak
,
D.
,
Luning Prak
,
P. J.
,
Trulove
,
P.
, and
Cowart
,
J. S.
,
2016
, “
Formulation of Surrogate Mixtures Based on Physical and Chemical Analysis of Hydrodepolymerized Cellulosic Diesel Fuel
,”
Energy Fuels
,
30
(
9
), pp.
7331
7341
.
12.
Chun
,
K. M.
, and
Heywood
,
J. B.
,
1987
, “
Estimating Heat Release and Mass of Mixture Burned From SI Engine Pressure Data
,”
Combust. Sci. Technol.
,
54
(
1–6
), pp.
133
143
.
13.
Gatowski
,
J. A.
,
Balles
,
E. N.
,
Chun
,
K. M.
,
Nelson
,
F. E.
,
Ekchian
,
J. A.
, and
Heywood
,
J. B.
,
1984
, “
Heat Release Analysis of Engine Pressure Data
,”
SAE
Paper No. 841359.
14.
MathWorks, Nonlinear Regression,” MathWorks, Natick, MA, accessed Mar. 26, 2019, https://www.mathworks.com/discovery/nonlinear-regression.html
15.
MathWorks, “Fit Data With a Shallow Neural Network,” MathWorks, Natick, MA, accessed Mar. 26, 2019, https://www.mathworks.com/help/nnet/gs/fit-data-with-a-neural-network.html
16.
MathWorks, “Improve Shallow Neural Network Generalization and Avoid Overfitting,” MathWorks, Natick, MA, accessed Mar. 26, 2019, https://www.mathworks.com/help/nnet/ug/improve-neural-network-generalization-and-avoid-overfitting.html
17.
Kohonen
,
T.
,
1995
,
Self-Organizing Maps
(Springer Series in Information Sciences), Vol.
30
,
Springer
,
Berlin
.
18.
Laboratory of Computer and Information Science — Adaptive Informatics Research Centre, 2019, “Homepage of SOM Toolbox,” Helsinki University of Technology, Espoo, Finland, accessed Mar. 26, 2019, http://www.cis.hut.fi/somtoolbox/
19.
Laboratory of Computer and Information Science — Adaptive Informatics Research Centre, 2019, “Documentation,” Helsinki University of Technology, Espoo, Finland, accessed March 26, 2019, http://www.cis.hut.fi/somtoolbox/documentation/
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