In this paper, a dynamic neural network (DNN) based on robust identification scheme is presented to determine compressor surge point accurately using sensor fault detection (FD). The main innovation of this paper is to present different and complementary technique for surge suppressing studies within sensor FD. The proposed method aims to utilize the embedded analytical redundancies for sensor FD, even in the presence of uncertainty in the compressor and sensor noise. The robust dynamic neural network is developed to learn the input–output map of the compressor for residual generation and the required data is obtained from the compressor Moore–Greitzer simulated model. Generally, the main drawback of DNN method is the lack of systematic law for selecting of initial Hurwitz matrix. Therefore, the subspace identification method is proposed for selecting this matrix. A number of simulation studies are carried out to demonstrate the advantages, capabilities, and performance of our proposed FD scheme and a worthwhile direction for future research is also presented.

References

References
1.
Bloch
,
H. P.
,
2006
,
Application Guide to Compressor Technology
,
2nd ed.
,
Wiley
,
New York
.
2.
Bloch
,
H. P.
,
2006
,
Compressors and Modern Applications
,
2nd ed.
,
Wiley
,
New York
, pp.
115
127
.
3.
Helivort
,
J. V.
,
2007
, “
Centrifugal Compressor Surge Modeling and Identification for Control
,” Ph.D. thesis, Eindhoven University of Technology, Eindhoven, Netherlands.
4.
Billam
,
M. R.
,
2011
, “
Compressors Used in Oil & Gas Industry
,” Dresser-Rand, Olean, NY, available at: http://www.ipt.ntnu.no/~jsg/undervisning/naturgass/lysark/LyarkBillam2011.pdf
5.
Gatewood
,
J.
,
2012
, “
Future Compressor Station Technologies and Applications
,”
Gas Electric Partnership Conference
, Southwest Research Institute.
6.
Betta
,
G.
, and
Pietrosanto
,
A.
,
2000
, “
Instrument Fault Detection and Isolation: State of the Art and New Research Trends
,”
IEEE Trans. Instrum. Meas.
,
49
(
1
), pp.
100
–107.10.1109/19.836318
7.
Qin
,
S.
, and
Li
,
W.
,
2001
, “
Detection and Identification of Faulty Sensors in Dynamic Processes
,”
AIChE J.
,
47
(
7
), pp.
1581
1593
.10.1002/aic.690470711
8.
Sami Shaker
,
M.
,
2012
, “
Active Fault-Tolerant Control of Nonlinear Systems With Wind Turbine Application
,” Ph.D. thesis, University of Hull, Yorkshire, UK.
9.
Alag
,
S.
,
Agogino
,
A.
, and
Morjaria
,
M.
,
2001
, “
A Methodology for Intelligent Sensor Measurement, Validation, Fusion, and Fault Detection for Equipment Monitoring and Diagnostics. (AI EDAM)
,”
Artificial Intell. Eng. Design, Anal. Manufacturing
,
15
(
4
), pp.
307
320
.10.1017/S0890060401154053
10.
Jiang
,
L.
,
2011
, “
Sensor Fault Detection and Isolation Using System Dynamics Identification Techniques
,” Ph.D. thesis, The University of Michigan, Ann Arbor, MI.
11.
Pike
,
P.
, and
Pennycook
,
K.
,
1992
, Commissioning of BEMS: Code of Practice, Building Services Research & Information Association, Berkshire, UK.
12.
Keliris
,
C.
,
Polycarpou
,
M.
, and
Parisini
,
T.
,
2013
, “
A Distributed Fault Detection Filtering Approach for a Class of Interconnected Continuous-Time Nonlinear Systems
,”
IEEE Trans. Auto. Control
,
58
(
8
), pp.
2032
2047
.10.1109/TAC.2013.2253231
13.
Patton
,
R. J.
,
Frank
,
P. M.
, and
Clark
,
R. N.
,
2000
,
Issues in Fault Diagnosis for Dynamic Systems
,
Springer
,
New York
.
14.
Qayyum Khan
.
A.
,
2010
, “
Observer-Based Fault Detection in Nonlinear Systems
,” Ph.D. thesis, University of Duisburg-Essen, Essen, Germany.
15.
Ding
,
S. X.
,
2012
, “
Data-Driven Design of Model-Based Fault Diagnosis Systems
,”
8th IFAC Symposium on Advanced Control of Chemical Processes, The International Federation of Automatic Control
, Furama Riverfront, Singapore, July 10–13.
16.
Terra
,
M. H.
, and
Tinos
,
R.
,
2001
, “
Fault Detection and Isolation in Robotic Manipulators Via Neural Networks: A Comparison Among Three Architectures for Residual Analysis
,”
J. Robot. Syst.
,
18
(
7
), pp.
357
374
.10.1002/rob.1029
17.
Bakshi
,
B. R.
, and
Stephanopoulos
,
G.
,
1993
, “
Wave-Net: A Multiresolution, Hierarchical Neural Network With Localized Learning
,”
J. Am. Inst. Chem. Eng.
,
39
(
1
), pp.
57
81
.10.1002/aic.690390108
18.
Guo
,
T. H.
, and
Nurre
,
J.
,
1991
, “
Sensor Failure Detection and Recovery by Neural Networks
,”
International Joint Conference on Neural Networks
(
IJCNN-91-Seattle
), Seattle, WA, July 8–14, pp.
221
226
.10.1109/IJCNN.1991.155180
19.
Perla
,
R.
,
Mukhopadhyay
,
S.
, and
Samanta
,
A.
,
2004
, “
Sensor Fault Detection and Isolation Using Artificial Neural Networks
,”
IEEE Region 10 Conference
(
TENCON 2004
), Chiang Mai, Thailand, November 21–24, pp.
676
679
.10.1109/TENCON.2004.1415023
20.
Frank
,
P.
,
1987
, “Fault Diagnosis in Dynamic Systems Via State Estimation—A Survey,”
System Fault Diagnostics, Reliability, and Related Knowledge-Based Approaches
, Vol. 1, D. Reidel Publishing Co., Dordrecht, Netherlands, pp.
35
98
.10.1007/978-94-009-3929-5_2
21.
Pham
,
D.
, and
Liu
,
X.
,
1992
, “
Dynamic System Identification Using Partially Recurrent Neural Networks
,” Neural Networks for Identification, Prediction and Control, Springer-Verlag, London, pp.
47
61
.
22.
Deng
,
Jiamei
,
2013
, “
Dynamic Neural Networks With Hybrid Structures for Nonlinear System Identification
,”
Eng. Appl. Artificial Intell.
,
26
(
1
), pp.
281
292
.10.1016/j.engappai.2012.05.003
23.
Gupta
,
M.
,
Jin
,
L.
, and
Homma
,
N.
,
2003
,
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
,
Wiley
,
New York
.
24.
Funahashi
,
K.
, and
Nakamura
,
Y.
,
1993
, “
Approximation of Dynamic Systems by Continuous-Time Recurrent Neural Networks
,”
Neural Networks
,
6
(
6
), pp.
801
806
.10.1016/S0893-6080(05)80125-X
25.
Polycarpou
,
M.
, and
Ioannou
,
P.
,
1991
, “
Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis
,” University of Southern California, Los Angeles, CA, Systems Report No. 91-09-01.
26.
Wen
,
Y.
,
Poznyak
,
A.
, and
Xiaoou
,
L.
,
2001
, “
Multilayer Dynamic Neural Networks for Non-Linear System On-Line Identification
,”
Int. J. Control
,
74
(
18
), pp.
1858
1864
.10.1080/00207170110089816
27.
Poznyak
,
A. S.
,
Wen
,
Y.
,
Poznyak
,
T. I.
, and
Najim
,
K.
,
2004
, “
Simultaneous States and Parameters Estimation of an Ozonation Reactor Based on Dynamic Neural Network,” Diff. Equ. Dyn. Syst.
,
12
(
1–2
), pp.
195
221
.
28.
García
,
A.
,
Poznyak
,
A.
,
Chairez
,
I.
, and
Poznyak
,
T.
,
2008
, “
Projectional Differential Neural Network Observer With Stable Adaptation Weights
,”
47th IEEE Conference on Decision and Control
(
CDC 2008
), Cancun, Mexico, December 9–11.10.1109/CDC.2008.4738950
29.
Mirak
,
S. M.
,
2013
, “
Neural Network-Based Fault Diagnosis of Satellites Formation Flight
,” M.Sc. thesis, Concordia University, Portland, OR.
30.
Li
,
L.
,
Ma
,
L.
, and
Khorasani
,
K.
,
2005
, “A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite’s Actuator/Thruster Failures,”
Advances in Neural Networks – ISNN 2005
(Lecture Notes in Computer Science, Vol. 3498), Springer, Berlin, pp.
574
583
.10.1007/11427469_92
31.
Valdes
,
A.
, and
Khorasani
,
K.
,
2010
, “
A Pulsed Plasma Thruster Fault Detection and Isolation Strategy for Formation Flying of Satellites
,”
Appl. Soft Comput.
,
10
(
3
), pp.
746
758
.10.1016/j.asoc.2009.09.005
32.
Patan
,
K.
, and
Parisini
,
T.
,
2005
, “
Identification of Neural Dynamic Models for Fault Detection and Isolation: The Case of a Real Sugar Evaporation Process
,”
IFAC J. Process Control
,
15
(
1
), pp.
67
79
.10.1016/j.jprocont.2004.04.001
33.
Greitzer
,
E. M.
,
1997
, “
Surge and Rotating Stall in Axial Flow Compressors: Part I—Theoretical Compression System Model
,”
ASME J. Eng. Power
,
98
(
2
), pp.
190
198
.10.1115/1.3446138
34.
Greitzer
,
E. M.
,
1997
, “
Surge and Rotating Stall in Axial Flow Compressors: Part II—Experimental Results and Comparison With Theory
,”
ASME J. Eng. Power
,
98
(
2
), pp.
199
217
.10.1115/1.3446139
35.
Gravdahl
,
J. T.
, and
Egeland
O.
,
1999
,
Compressor Surge and Rotating Stall: Modelling and Control
,
Springer
,
New York
.
36.
Guoxiang
,
G.
,
Andrew
,
S.
, and
Calin
B.
,
1999
, “
Stability Analysis for Rotating Stall Dynamics in Axial Flow Compressors
,”
Circuits, Syst., Signal Process.
,
18
(
4
), pp.
331
350
.10.1007/BF01200786
37.
Christensen
,
D.
,
Armor
,
J.
,
Dhingra
,
M.
,
Cantin
,
P.
,
Gutz
,
D.
,
Neumeier
,
Y.
,
Prasad
,
J. V.
,
Szucs
,
A.
, and
Wadia
,
R.
,
2008
, “
Development and Demonstration of a Stability Management System for Gas Turbine Engines
ASME J. Turbomach.
,
130
(
3
), p.
031011
.10.1115/1.2777176
38.
Hunt
,
K. J.
,
Sbarbaro
,
D.
,
Zbikowski
,
R.
, and
Gawthrop
,
P. J.
,
1992
, “
Neural Networks for Control Systems—A Survey
,”
Automatica
,
28
(
6
), pp.
1083
1112
.10.1016/0005-1098(92)90053-I
39.
Hush
,
D. R.
, and
Horne
,
B. G.
,
1993
, “
Progress in Supervised Neural Networks
,”
IEEE Signal Process
,
10
(
1
), pp.
8
39
.10.1109/79.180705
40.
Tayarani-Bathaie
,
S. S.
,
Sadough Vanini
,
Z. N.
, and
Khorasani
,
K.
,
2013
, “
Dynamic Neural Network-Based Fault Diagnosis of Gas Turbine Engines
,”
Neuro Computing
, Vol. 125,
Elsevier
,
New York
, pp. 153–165.
41.
Dinh
,
H.
,
2012
, “
Dynamic Neural Network-Based Robust Control Methods for Uncertain Nonlinear Systems
,” Ph.D. thesis, University of Florida, Gainesville, FL.
42.
Lewis
,
F. L.
,
Selmic
,
R.
, and
Campos
,
J.
,
2002
,
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
,
SIAM
,
Philadelphia, PA
.
43.
Garcia
,
B.
Poznyak
,
A.
Chairez
,
I.
and
Poznyak
T.
,
2007
, “
Projectional Dynamic Neural Network Observer
,”
3rd IFAC Symposium on System, Structure and Control
, Foz do Iguaçu, Brazil, October 17–19.10.3182/20071017-3-BR-2923.00028.
44.
Van Overschee
,
P.
,
De Moor
,
B.
,
1994
, “
N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems
,”
Automatica
,
30
(
1
), pp.
75
93
.10.1016/0005-1098(94)90230-5
45.
Trnka
,
P.
,
2005
, “
Subspace Identification Methods, Technical Report
,” Czech Technical University in Prague, Prague, Czech Republic.
46.
Naik
,
A. S.
,
2010
, “
Subspace Based Data-Driven Designs of Fault Detection Systems
,” Ph.D. thesis, University Duisburg-Essen, Essen, Germany.
47.
Poznyak
,
A. S.
,
Sanchez
,
E. N.
, and
Wen
,
Y.
,
2001
,
Differential Neural Networks for Robust Nonlinear Control Identification, State Estimation and Trajectory Tracking, World Scientific
, Singapore.
48.
Verhaegen
,
M.
, and
Dewilde
,
P.
,
1992
, “
Subspace Model Identification, Part 1: The Output-Error State-Space Model Identification Class of Algorithms
,”
Int. J. Control
,
56
(
5
), pp.
1187
1210
.10.1080/00207179208934363
You do not currently have access to this content.