Research Papers

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

[+] Author and Article Information
Dengji Zhou, Tingting Wei, Huisheng Zhang, Shixi Ma

School of Mechanical Engineering,
Gas Turbine Research Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China

Fang Wei

AECC Commercial Aircraft,
Engine Co., Ltd.,
Shanghai 200241, China

1Corressponding author.

Manuscript received February 16, 2017; final manuscript received July 8, 2017; published online October 4, 2017. Assoc. Editor: Michael Beer.

ASME J. Risk Uncertainty Part B 4(2), 021005 (Oct 04, 2017) (8 pages) Paper No: RISK-17-1026; doi: 10.1115/1.4037328 History: Received February 16, 2017; Revised July 08, 2017

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.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


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. [CrossRef]
Zedda, M. , and Singh, R. , 2012, “ Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques,” J. Propul. Power, 18(5), pp. 1019–1025. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
Guo, H. Y. , 2006, “ Structural Damage Detection Using Information Fusion Technique,” Mech. Syst. Signal Process., 20(5), pp. 1173–1188. [CrossRef]
Zhang, J. , 2006, “ Improved On-Line Process Fault Diagnosis Through Information Fusion in Multiple Neural Networks,” Comput. Chem. Eng., 30(3), pp. 558–571. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Hall, D. L. , and Llinas, J. , 1997, “ An Introduction to Multisensor Data Fusion,” Proc. IEEE, 85(1), pp. 6–23. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Dasarathy, B. V. , 1997, “ Sensor Fusion Potential Exploitation-Innovative Architectures and Illustrative Applications,” Proc. IEEE, 85(1), pp. 24–38. [CrossRef]
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=
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.
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. [CrossRef]
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).
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. [CrossRef]
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. [CrossRef]


Grahic Jump Location
Fig. 1

Relationship between belief function and plausibility function of event A

Grahic Jump Location
Fig. 5

Fusion frame of intake icing diagnosis

Grahic Jump Location
Fig. 2

Diagnostic process without information fusion

Grahic Jump Location
Fig. 3

Design steps of fusion diagnostic

Grahic Jump Location
Fig. 4

Configuration of target gas turbine

Grahic Jump Location
Fig. 6

Fusion frame A of sensor fault diagnosis

Grahic Jump Location
Fig. 7

Fusion frame B of sensor fault diagnosis

Grahic Jump Location
Fig. 8

Curve for ambient temperature

Grahic Jump Location
Fig. 9

Curve for dew point temperature

Grahic Jump Location
Fig. 17

Diagnostic result of compressor discharge temperature with bias

Grahic Jump Location
Fig. 18

Diagnostic result of compressor discharge temperature with drift

Grahic Jump Location
Fig. 10

m1 of intake icing fault diagnosis

Grahic Jump Location
Fig. 11

m2 of intake icing fault diagnosis

Grahic Jump Location
Fig. 12

m3 of intake icing fault diagnosis

Grahic Jump Location
Fig. 13

Diagnostic result based on information fusion of intake icing fault diagnosis

Grahic Jump Location
Fig. 14

Measurement diagnostic result without sensor faults based on method A

Grahic Jump Location
Fig. 15

Measurement diagnostic result without sensor faults based on method B

Grahic Jump Location
Fig. 16

Implantable measuring errors of compressor outlet temperature sensor




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In