An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved Dempster–Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

References

References
1.
Simani
,
S.
, and
Patton
,
R. J.
,
2008
, “
Fault Diagnosis of an Industrial Gas Turbine Prototype Using a System Identification Approach
,”
Control Eng. Pract.
,
16
(
7
), pp.
769
786
.
2.
Zedda
,
M.
, and
Singh
,
R.
,
2012
, “
Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques
,”
J. Propul. Power
,
18
(
5
), pp.
1019
1025
.
3.
Niu
,
G.
,
Yang
,
B. S.
, and
Pecht
,
M.
,
2010
, “
Development of an Optimized Condition-Based Maintenance System by Data Fusion and Reliability-Centered Maintenance
,”
Reliab. Eng. Syst. Saf.
,
95
(
7
), pp.
786
796
.
4.
Kraft
,
J.
,
Sethi
,
V.
, and
Singh
,
R.
,
2014
, “
Optimization of Aero Gas Turbine Maintenance Using Advanced Simulation and Diagnostic Methods
,”
ASME J. Eng. Gas Turbines Power
,
136
(
11
), p.
111601
.
5.
Tayarani-Bathaie
,
S. S.
,
Vanini
,
Z. N. S.
, and
Khorasani
,
K.
,
2014
, “
Dynamic Neural Network-Based Fault Diagnosis of Gas Turbine Engines
,”
Neurocomputing
,
125
, pp.
153
165
.
6.
Zhou
,
D.
,
Zhang
,
H.
, and
Weng
,
S.
,
2015
, “
A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine
,”
ASME J. Eng. Gas Turbines Power
,
137
(
10
), p.
102605
.
7.
Gayme
,
D.
,
Menon
,
S.
,
Ball
,
C.
,
Mukavetz
,
D.
, and
Nwadiogbu
,
E.
,
2003
, “
Fault Diagnosis in Gas Turbine Engines Using Fuzzy Logic
,”
IEEE International Conference on Systems, Man and Cybernetics
(
CMSC
), Washington, DC, Oct. 5–8, pp.
3756
3762
.
8.
Cai
,
B.
,
Liu
,
Y.
,
Fan
,
Q.
,
Zhang
,
Y.
,
Liu
,
Z.
,
Yu
,
S.
, and
Ji
,
R.
,
2014
, “
Multi-Source Information Fusion Based Fault Diagnosis of Ground-Source Heat Pump Using Bayesian Network
,”
Appl. Energy
,
114
, pp.
1
9
.
9.
Guo
,
H. Y.
,
2006
, “
Structural Damage Detection Using Information Fusion Technique
,”
Mech. Syst. Signal Process.
,
20
(
5
), pp.
1173
1188
.
10.
Zhang
,
J.
,
2006
, “
Improved On-Line Process Fault Diagnosis Through Information Fusion in Multiple Neural Networks
,”
Comput. Chem. Eng.
,
30
(
3
), pp.
558
571
.
11.
Rapur
,
J. S.
, and
Tiwari
,
R.
,
2017
, “
Experimental Time-Domain Vibration-Based Fault Diagnosis of Centrifugal Pumps Using Support Vector Machine
,”
ASME J. Risk Uncertainty Eng. Syst., Part B
,
3
(
4
), p.
044501
.
12.
Nakamura
,
E. F.
,
Loureiro
,
A. A. F.
, and
Frery
,
A. C.
,
2007
, “
Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications
,”
ACM Comput. Surv.
,
39
(
3
), p.
9
.
13.
Hall
,
D. L.
, and
Llinas
,
J.
,
1997
, “
An Introduction to Multisensor Data Fusion
,”
Proc. IEEE
,
85
(
1
), pp.
6
23
.
14.
Blum
,
R. S.
,
Kassam
,
S. A.
, and
Poor
,
H. V.
,
1997
, “
Distributed Detection With Multiple Sensors I. Advanced Topics
,”
Proc. IEEE
,
85
(
1
), pp.
64
79
.
15.
Yang
,
Y.
,
Jing
,
Z.
,
Gao
,
T.
, and
Wang
,
H.
,
2007
, “
Multi-Sources Information Fusion Algorithm in Airborne Detection Systems
,”
J. Syst. Eng. Electron.
,
18
(
1
), pp.
171
176
.
16.
Xu
,
X. B.
,
2009
, “
Information Fusion Algorithm of Fault Diagnosis Based on Random Set Metrics of Fuzzy Fault Features
,”
J. Electron. Inf. Technol.
,
31
(
7
), pp.
1635
1640
.
17.
Ribeiro
,
R. A.
,
Falcão
,
A.
,
Mora
,
A.
, and
Fonseca
,
J. M
.
,
2014
, “
FIF: A Fuzzy Information Fusion Algorithm Based on Multi-Criteria Decision Making
,”
Knowl.-Based Syst.
,
58
, pp.
23
32
.
18.
Lin
,
G.
,
Liang
,
J.
, and
Qian
,
Y.
,
2015
, “
An Information Fusion Approach by Combining Multigranulation Rough Sets and Evidence Theory
,”
Inf. Sci.
,
314
(
1
), pp.
184
199
.
19.
Corotis
,
R.
,
2015
, “
An Overview of Uncertainty Concepts Related to Mechanical and Civil Engineering
,”
ASME J. Risk Uncertainty Eng. Syst., Part B
,
1
(
4
), p.
040801
.
20.
Dasarathy
,
B. V.
,
1997
, “
Sensor Fusion Potential Exploitation-Innovative Architectures and Illustrative Applications
,”
Proc. IEEE
,
85
(
1
), pp.
24
38
.
21.
Volponi
,
A. J.
,
Brotherton
,
T.
,
Luppold
,
R.
, and
Simon
,
D. L.
,
2004
, “
Development of an Information Fusion System for Engine Diagnostics and Health Management
,” Glenn Research Center, National Aeronautics and Space Administration, Cleveland, OH, Report No.
NASA/TM–2004-212924
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.959&rep=rep1&type=pdf.
22.
Ma
,
S. X.
,
Zhou
,
D. J.
, and
Zhang
,
H. S.
,
2016
, “
SA-PSO Hybrid Algorithm for Gas Path Diagnostics of Gas Turbine
,”
16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery (ISROMAC)
, Honolulu, HI, Apr. 10–15, Paper No.
ISROMAC2016-394
http://isromac-isimet.univ-lille1.fr/upload_dir/finalpaper/394.finalpaper.pdf.
23.
Basir
,
O.
, and
Yuan
,
X.
,
2007
, “
Engine Fault Diagnosis Based on Multi-Sensor Information Fusion Using Dempster–Shafer Evidence Theory
,”
Inf. Fusion
,
8
(
4
), pp.
379
386
.
24.
Salehpour-Oskouei
,
F.
, and
Pourgol-Mohammad
,
M.
,
2017
, “
Risk Assessment of Sensor Failures in a Condition Monitoring Process; Degradation-Based Failure Probability Determination
,”
Int. J. Syst. Assur. Eng. Manage.
(epub).
25.
Sofi
,
A.
,
Muscolino
,
G.
, and
Elishakoff
,
I.
,
2015
, “
Special Issue on Nonprobabilistic Treatments of Uncertainty: Recent Developments
,”
ASME J. Risk Uncertainty Eng. Syst., Part B
,
1
(
4
), p.
040301
.
26.
Elgheriani
,
M.
,
Khan
,
F.
, and
Zuo
,
M. J.
,
2017
, “
Rare Event Analysis Considering Data and Model Uncertainty
,”
ASME J. Risk Uncertainty Eng. Syst., Part B
,
3
(
2
), p.
021008
.
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