Evidence fusion method for diesel engine fault based on multi-attribute group decision making

  • WANG Cheng-yuan ,
  • XU Jiu-jun ,
  • YAN Zhi-jun
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  • Marine Engineering College, Dalian Maritime University, Dalian 116026,China)

Received date: 2017-11-24

  Revised date: 2018-04-01

  Online published: 2024-06-27

Abstract

In view of accuracy of evidence theory in the stage of fault diagnosis decision fusion by incompleteness and uncertainty of fault information in complex mechanical systems, the method of fault evidence fusion based on multi-attribute group decision making was proposed. By using attribute analysis of multi-attribute group decision making, the weight of decision body based on element attribute set was calculated to reduce the difference of fusion evidence sources. Based on the similarity dependence of the target fault of the diesel engine, by using the reliability weight of the target fault, the conflicting focal element was redistributed to improve the accuracy of the evidence fusion. The results of R6105AZLD diesel engine bench test show that the proposed method can greatly improve diagnostic accuracy and robustness.

Cite this article

WANG Cheng-yuan , XU Jiu-jun , YAN Zhi-jun . Evidence fusion method for diesel engine fault based on multi-attribute group decision making[J]. Journal of Dalian Maritime University, 2018 , 44(3) : 71 -78 . DOI: 10.16411/j.cnki.issn1006-7736.2018.03.011

References

[1] YAGER R R. On the Dempster-Shafer Framework and New Combination Rules[J]. Information Sciences, 1987, 41[2]:93-137.
[2] Murphy C K. Combining belief functions when evidence conflicts[J]. Decision Support Systems, 2000, 29(1):1-9. [3] Ibrahim J G, Chen M, Gwon Yetal. The power prior: theory and applications[J]. Statistics in Medicine, 2015, 34(28): 3724-3749.
[4] SMARANDACHE F, DEZERT J. Advances and Applications of DSmT for Information Fusion Vol.3[M] .American Research Press,Rehoboth,2009.
[5] Bhalla D, Bansal R K, Gupta H O. Integrating AI Based DGA Fault Diagnosis Using Demper-Shafer Theory[J]. International Journal of Electronical Power&Energy System,2013,48(10): 31-38.
[6] TIMO S, HEIKKI N K, HANNU K. Neural Networks in process fault diagnosis[J]. IEEE Trans. On SMC, 1991,21(4):815-825.
[7] Climente-Alarcon V, Antonino-Daviu J A, RieraGuasp M. Induction motor diagnosis by advanced notch FIR filters and the Wigner-Ville distribution[j]. IEEE Transactions on Industrial Electronics, 2014,61(8): 4217-4227.
[8] Liu W. Analyzing the Degree of Conflict Among Belief Functions[J]. Artificial Intelligence, 2006, 171(11): 909-924.
[9] Jousselem AL, Liu C, Grenier D, et al. Measuring Ambiguity in the Evidence Theory[J]. IEEE Trans. on Systems, Man and Cybernetics-part A : Systems and Humans, 2006, 36(5):890-903.
[10] Liu P D, Wang Y M. Multiple attribute group decision making methods based on intuitionistic linguistic power generalized aggregation operators[J]. Applied Soft Computing, 2014, 17(1): 90-104.
[11] Smets P. The combination of evidence in the transferable belief model[J]. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1990, 12(5): 447-485.
[12]Haenni R. Are alternatives to Dempster’s rule of combination real alternatives Comments on about the belief function combination an the conflict management problem[J]. Information Fusion, 2001, 3(3): 237-239.
[13]Hua Zhongsheng, Gong Bengang, Xu Xiaoyan. ADS-AHPApproach for Multi-attribute Decision Making Problem with Incomplete Information [J]. Expert System withApplication, 2008, 34( 3) : 2221-2227.
[14]Carbajal J C, McLennan A, Tourky R. Truthful implementation and preference aggregation in restricted domains[J]. Journal of Economic Theory, 2013, 148(3): 1074-1101.
[15] Liu P D, Wang Y M. Multiple attribute group decision making methods based on intuitionistic linguistic power generalized aggregation operators[J]. Applied Soft Computing, 2014, 17(1): 90-104.
[16] Xu Z S. Group decision making based on multiple types of linguistic preference relations[J]. Information Sciences, 2008, 178(2): 452-467.
[17] 安春莲,黄静,吴耀云.基于证据理论的多源信息融合模型[J]. 电子信息对抗技术,2017,(01):23-26. doi:10.3969/j.issn. 1674-2230.2017.01.005.
AN Chunlian, HUANG Jing, WU Yaoyun. A Multi-Source Information Fusion Model Based on Evidence Theory[J]. Electronic Information Warfare Technology, 2011, 19(06):133-140. (in Chinese)
[18] Friedman N, Dan G, Goldszmidt M. Bayesian Network Classifiers[J]. Wiley Encyclopedia of Operations Research & Management Science, 2011 ,29(2-3):598-605.
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