Multi-source Data Fusion Method Based on Difference Information

doi:10.12068/j.issn.1005-3026.2021.09.005

Abstract

Abstract: D-S evidence theory can be applied to the field of multi-source data fusion. However， counter-intuitive results may come out when handing highly conflicting evidences. In order to solve this problem， a modified fusion method with the concept of difference information(DI)was proposed. First， information entropy indicated the relative importance of evidence， and divergence was used to obtain the credibility of evidence. Then， the evidence difference was optimized by the credibility of the evidence to obtain difference information(DI). The final weight of the calculated data was used as the basic probability distribution in D-S evidence theory for decision-making. Compared with other methods in dealing with conflicting evidence， consistent evidence， and different amounts of evidence， the proposed method converges faster and has higher accuracy. The application examples of fault diagnosis show that the proposed method has less uncertainty and is better than other existing methods.

Key words: difference information(DI); D-S evidence theory; multi-source data; information fusion; decision-making

CLC Number:

TP311
TP181

WANG Shu， REN Yu， GUAN Zhan-xu， WANG Jing. Multi-source Data Fusion Method Based on Difference Information[J]. Journal of Northeastern University(Natural Science), 2021, 42(9): 1246-1253.

References

[1]Amyotte K P.Safety analysis in process facilities:comparison of fault tree and Bayesian network approaches［J］.Reliability Engineering & System Safety，2011，96(8):925-932.
[2]Seixas F L，Zadrozny B，Laks J，et al.A Bayesian network decision model for supporting the diagnosis of dementia，Alzheimers disease and mild cognitive impairment［J］.Computers in Biology and Medicine，2014，51C(7):140-158.
[3]Mori J，Mahal E V.Planning and scheduling of steel plates production.Part I:estimation of production times via hybrid Bayesian networks for large domain of discrete variables［J］.Computers & Chemical Engineering，2015，79(4):113-134.
[4]Mori J，Mahalec V，Yu J.Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes［J］.Computers & Chemical Engineering，2014，71:171-209.
[5]Jiang W，Wei B.Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making［J］.International Journal of Systems Science，2018，49(1/2/3/4):582-594.
[6]Wen J，Hu W.An improved soft likelihood function for Dempster-Shafer belief structures［J］.International Journal of Intelligent Systems，2018，33(6):1264-1282.
[7]Kang B，Zhang P，Gao Z，et al.Environmental assessment under uncertainty using Dempster-Shafer theory and Z-numbers［J］.Journal of Ambient Intelligence and Humanized Computing，2020，11:2041-2060.
[8]Xiao F.A multiple-criteria decision-making method based on D numbers and belief entropy［J］.International Journal of Fuzzy Systems，2019，21(4):1144-1153.
[9]Seiti H，Hafezalkotob A，Martínez L.R-numbers，a new risk modeling associated with fuzzy numbers and its application to decision making［J］.Information Sciences，2019，483:206-231.
[10]Dempster A P.Upper and lower probabilities induced by a multivalued mapping［J］.Annals of Mathematical Statistics，1967，38(2):325-339.
[11]Lefevre E，Colot O，Vannoorenberghe P.Belief function combination and conflict management［J］.Information Fusion，2002，3(2):149-162.
[12]Yager R R.On the Dempster-Shafer framework and new combination rules［J］.Information Sciences，1987，41(2):93-137.
[13]Murphy C K.Combining belief functions when evidence conflicts［J］.Decision Support Systems，2000，29(1):1-9.
[14]Han D，Deng Y，Liu Q.Combining belief functions based on distance of evidence［J］.Decision Support Systems，2005，38(3):489-493.
[15]Zhang Z，Liu T，Chen D，et al.Novel algorithm for identifying and fusing conflicting data in wireless sensor networks［J］.Sensors，2014(6):9562-9581.
[16]Lin J.Divergence measures based on the shannon entropy［J］.IEEE Transaction on Information Theory，1991，37(1):145-151.
[17]Shafer G A.A mathematical theory of evidence［J］.Technometrics，1978，20(1):106.
[18]Xiao F.Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy［J］.Information Fusion，2018，46:23-32.
[19]Dong Y，Zhang J，Li Z，et al.Combination of evidential sensor reports with distance function and belief entropy in fault diagnosis［J］.International Journal of Computers，Communications & Control(IJCCC)，2019，14(3):329-343.
[20]Liu Z，Dezert J，Quan P，et al.Combination of sources of evidence with different discounting factors based on a new dissimilarity measure［J］.Decision Support Systems，2012，52(1):133-141.
[21]Ma M，Jiyao A N.Combination of evidence with different weighting factors: a novel probabilistic-based dissimilarity measure approach［J］.Journal of Sensors，2015，2015(2):1-9.
[22]Jiang W，Wei B Y，Xie C H，et al.An evidential sensor fusion method in fault diagnosis［J］.Advances in Mechanical Engineering，2016，8(3):1-7.
[23]Fan X ，Zuo M J.Fault diagnosis of machines based on D-S evidence theory.Part 1:D-S evidence theory and its improvement［J］.Elsevier Science Direct，2006，27(5):266-376.
[24]Yuan K，Xiao F，Fei L，et al.Modeling sensor reliability in fault diagnosis based on evidence theory［J］.Sensors，2016，16(1):113.