Abstract
To deal with class imbalance learning (CIL) problems, a novel algorithm is proposed based on kernel extreme learning machine (KELM), named KELM-CIL. To solve it, two algorithms are developed from the dual and primal spaces, respectively, thus yielding D-KELM-CIL and P-KELM-CIL. However, both D-KELM-CIL and P-KELM-CIL are not sparse algorithms. Hence, a sparse strategy based on Cholesky factorization is utilized to realize their sparseness, producing CD-KELM-CIL and CP-KELM-CIL. For large-size problems, a probabilistic trick is applied to accelerate them further, hence obtaining PCD-KELM-CIL and PCP-KELM-CIL. To test the effectiveness and efficacy of the proposed algorithms, experiments on benchmark datasets are carried out. When the proposed algorithms are applied to fault detection of aircraft engine, they show good generalization performance and real-time performance, especially for CP-KELM-CIL and PCP-KELM-CIL, which indicates that they can be developed as candidate techniques for fault detection of aircraft engine.
Issue Section:
Research Papers
References
1.
Kobayashi
,
T.
, and
Simon
,
D. L.
, 2007
, “
Integration of on-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management
,” ASME ASME J. Eng. Gas Turbines Power
,
129
(4
), pp. 986
–993
.10.1115/1.27476402.
Sadough Vanini
,
Z.
,
Khorasani
,
K.
, and
Meskin
,
N.
, 2014
, “
Fault Detection and Isolation of a Dual Spool Gas Turbine Engine Using Dynamic Neural Networks and Multiple Model Approach
,” Inf. Sci.
,
259
, pp. 234
–251
.10.1016/j.ins.2013.05.0323.
Sina Tayarani-Bathaie
,
S.
,
Sadough Vanini
,
Z.
, and
Khorasani
,
K.
, 2014
, “
Dynamic Neural Network-Based Fault Diagnosis of Gas Turbine Engines
,” Neurocomputing
,
125
, pp. 153
–165
.10.1016/j.neucom.2012.06.0504.
Kobayashi
,
T.
, and
Simon
,
D. L.
, 2003
, “
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
,” National Aeronautics and Space Administration, Washington, DC, Report No. NASA/TM-2003-212526
.https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20030067984.pdf5.
Borguet
,
S.
, and
Leonard
,
O.
, 2009
, “
Coupling Principal Component Analysis and Kalman Filtering Algorithms for on-Line Aircraft Engine Diagnostics
,” Control Eng. Pract.
,
17
(4
), pp. 494
–502
.10.1016/j.conengprac.2008.09.0086.
Simon
,
D. L.
, and
Garg
,
S.
, 2009
, “
Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation
,” ASME
Paper No. GT2009-59684.10.1115/GT2009-596847.
Armstrong
,
J. B.
, and
Simon
,
D. L.
, 2012
, “
Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications
,” Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012
(PHM 2012
), Minneapolis, MN, Sept. 23–27, pp. 134
–146
.https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20130001700.pdf8.
Simon
,
D. L.
, and
Rinehart
,
A. W.
, 2016
, “
Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics
,” ASME J. Eng. Gas Turbines Power
,
138
(7
), p. 071201.10.1115/1.40323399.
Kyriazis
,
A.
, and
Mathioudakis
,
K.
, 2009
, “
Gas Turbine Fault Diagnosis Using Fuzzy-Based Decision Fusion
,” J. Propul. Power
,
25
(2
), pp. 335
–343
.10.2514/1.3862910.
Schwabacher
,
M.
, and
Goebel
,
K.
, 2007
, “
A Survey of Artificial Intelligence for Prognostics
,” Artificial Intelligence for Prognostics
, Arlington, VA, Nov. 9–11, pp. 107
–114
.https://ti.arc.nasa.gov/m/pub-archive/1382h/1382%20(Schwabacher).pdf11.
Shahzad
,
K.
, and
Manarvi
,
I. A.
, 2012
, “
Defect Trend Analysis of t56 Engine After Overhaul
,” IEEE Aerospace Conference Proceedings
, Big Sky, MT, Mar. 3–10, pp. 1
–6
.10.1109/AERO.2012.618738112.
Naderi
,
E.
, and
Khorasani
,
K.
, 2018
, “
Data-Driven Fault Detection, Isolation and Estimation of Aircraft Gas Turbine Engine Actuator and Sensors
,” Mech. Syst. Signal Process.
,
100
, pp. 415
–438
.10.1016/j.ymssp.2017.07.02113.
He
,
H.
, and
Garcia
,
E. A.
, 2009
, “
Learning From Imbalanced Data
,” IEEE Trans. Knowl. Data Eng.
,
21
(9
), pp. 1263
–1284
.10.1109/TKDE.2008.23914.
Yang
,
Q.
, and
Wu
,
X.
, 2006
, “
10 Challenging Problems in Data Mining Research
,” Int. J. Inf. Technol. Decis. Making
,
5
(4
), pp. 597
–604
.10.1142/S021962200600225815.
Vapnik
,
V. N.
, 1995
, The Nature of Statistical Learning Theory
,
Springer
,
New York
.16.
Cortes
,
C.
, and
Vapnik
,
V.
, 1995
, “
Support-Vector Networks
,” Mach. Learn.
,
20
(3
), pp. 273
–297
.10.1007/BF0099401817.
Vapnik
,
V.
, 1999
, “
An Overview of Statistical Learning Theory
,” IEEE Trans. Neural Networks
,
10
(5
), pp. 988
–999
.10.1109/72.78864018.
Liu
,
Y.-H.
, and
Chen
,
Y.-T.
, 2007
, “
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
,” IEEE Trans. Neural Networks
,
18
(1
), pp. 178
–192
.10.1109/TNN.2006.88301319.
Batuwita
,
R.
, and
Palade
,
V.
, 2010
, “
Fsvm-Cil: Fuzzy Support Vector Machines for Class Imbalance Learning
,” IEEE Trans. Fuzzy Syst.
,
18
(3
), pp. 558
–571
.10.1109/TFUZZ.2010.204272120.
Dai
,
H.-L.
, 2015
, “
Class Imbalance Learning Via a Fuzzy Total Margin Based Support Vector Machine
,” Appl. Soft Comput.
,
31
, pp. 172
–184
.10.1016/j.asoc.2015.02.02521.
Huang
,
G.-B.
,
Zhu
,
Q.-Y.
, and
Siew
,
C.-K.
, 2004
, “
Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks
,” IEEE International Conference on Neural Networks
, Budapest, Hungary, July 25–29, pp. 985
–990
.10.1109/IJCNN.2004.138006822.
Huang
,
G.-B.
,
Zhu
,
Q.-Y.
, and
Siew
,
C.-K.
, 2006
, “
Extreme Learning Machine: Theory and Applications
,” Neurocomputing
,
70
(1–3
), pp. 489
–501
.10.1016/j.neucom.2005.12.12623.
Elanayar
,
S.
, and
Shin
,
Y. C.
, 1994
, “
Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems
,” IEEE Trans. Neural Networks
,
5
(4
), pp. 594
–603
.10.1109/72.29822924.
Park
,
J.
, and
Sandberg
,
I. W.
, 1991
, “
Universal Approximation Using Radial-Basis-Function Networks
,” Neural Computation
,
3
(2
), pp. 246
–257
.10.1162/neco.1991.3.2.24625.
Zhao
,
Y.-P.
,
Li
,
Z.-Q.
,
Xi
,
P.-P.
,
Liang
,
D.
,
Sun
,
L.
, and
Chen
,
T.-H.
, 2017
, “
Gram-Schmidt Process Based Incremental Extreme Learning Machine
,” Neurocomputing
,
241
, pp. 1
–17
.10.1016/j.neucom.2017.01.04926.
Huang
,
G.
,
Huang
,
G.-B.
,
Song
,
S.
, and
You
,
K.
, 2015
, “
Trends in Extreme Learning Machines: A Review
,” Neural Networks
,
61
, pp. 32
–48
.10.1016/j.neunet.2014.10.00127.
Scholk¨Opf
,
B.
,
Mika
,
S.
,
Burges
,
C. J. C.
,
Knirsch
,
P.
,
Muller
,
K.-R.
,
Ratsch
,
G.
, and
Smola
,
A. J.
, 1999
, “
Input Space Versus Feature Space in Kernel-Based Methods
,” IEEE Trans. Neural Networks
,
10
(5
), pp. 1000
–1017
.10.1109/72.78864128.
Huang
,
G.-B.
,
Wang
,
D. H.
, and
Lan
,
Y.
, 2011
, “
Extreme Learning Machines: A Survey
,” Int. J. Mach. Learn. Cybern.
,
2
(2
), pp. 107
–122
.10.1007/s13042-011-0019-y29.
Zhao
,
Y.-P.
, 2016
, “
Parsimonious Kernel Extreme Learning Machine in Primal Via Cholesky Factorization
,” Neural Networks
,
80
, pp. 95
–109
.10.1016/j.neunet.2016.04.00930.
Zhang
,
X.
, 2004
, Matrix Analysis and Applications
,
Tsinghua University Press
,
Beijing, China
.31.
Scholk¨Opf
,
B.
,
Herbrich
,
R.
, and
Smola
,
A. J.
, 2001
, “
A Generalized Representer Theorem
,” Lecture Notes in Computer Science
, Springer,
Amsterdam, The Netherlands
, pp. 416
–426
.https://alex.smola.org/papers/2001/SchHerSmo01.pdf32.
Bottou
,
L.
,
Chapelle
,
O.
,
Decoste
,
D.
, and
Weston
,
J.
, 2007
, “
Training a Support Vector Machine in the Primal
,” Neural Comput.
,
19
(5
), pp. 1155
–1178
.10.1162/neco.2007.19.5.115533.
Zhao
,
Y.
, and
Sun
,
J.
, 2008
, “
Robust Support Vector Regression in the Primal
,” Neural Networks
,
21
(10
), pp. 1548
–1555
.10.1016/j.neunet.2008.09.00134.
Zhao
,
Y.
, and
Sun
,
J.
, 2009
, “
Recursive Reduced Least Squares Support Vector Regression
,” Pattern Recognit.
,
42
(5
), pp. 837
–842
.10.1016/j.patcog.2008.09.02835.
Caruana
,
R.
,
Lawrence
,
S.
, and
Giles
,
L.
, 2001
, “
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping
,” Advances in Neural Information Processing Systems
, MIT Press,
Denver, CO
.https://papers.nips.cc/paper/1895-overfitting-in-neural-nets-backpropagation-conjugate-gradient-and-early-stopping.pdf36.
Smola
,
A. J.
, and
Schölkopf
,
B.
, 2000
, “
Sparse Greedy Matrix Approximation for Machine Learning
,” Seventeenth International Conference on Machine Learning
, Stanford University, Stanford, CA, June 29–July 2, pp. 911
–918
. https://pdfs.semanticscholar.org/dd09/78a594290f6dc530e65983d79a056874185c.pdf37.
Borguet
,
S.
, and
Léonard
,
O.
, 2010
, “
Comparison of Adaptive Filters for Gas Turbine Performance Monitoring
,” J. Comput. Appl. Math.
,
234
(7
), pp. 2202
–2212
.10.1016/j.cam.2009.08.07538.
Pratt and Whintey
, 1997
, Module Analysis Program Network (MAPNET) Training Guide
,
Pratt & Whintey Customer Training Center
, Beijing, China.39.
Zhao
,
Y.-P.
,
Song
,
F.-Q.
,
Pan
,
Y.-T.
, and
Li
,
B.
, 2017
, “
Retargeting Extreme Learning Machines for Classification and Their Applications to Fault Diagnosis of Aircraft Engine
,” Aerosp. Sci. Technol.
,
71
, pp. 603
–618
.10.1016/j.ast.2017.10.00440.
Chawla
,
N. V.
,
Bowyer
,
K. W.
,
Hall
,
L. O.
, and
Kegelmeyer
,
W. P.
, 2002
, “
Smote: Synthetic Minority Oversampling Technique
,” J. Artif. Intell. Res.
,
16
, pp. 321
–357
.10.1613/jair.953Copyright © 2020 by ASME
You do not currently have access to this content.
