scholarly journals A New Method to Handle Conflict when Combining Evidences Using Entropy Function and Evidence Angle with an Effective Application in Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang

Addressing the problem of fusing highly conflicting evidences in Dempster–Shafer theory is one of the most necessary, important, and difficult research directions all the time, and so far we have published two papers related to it. In this paper, another novel method to handle conflict when combining evidences is proposed, where evidence distance, evidence angle, and improved entropy function, three key tools, are used for constructing the final weight of each body of evidence. This newly proposed approach mainly consists of three steps: firstly, both evidence distance and evidence angle determine the initial weight together; secondly, making use of the improved entropy modifies the initial weight to get the final weight; lastly, the classical D-S combination rule will be applied to obtain final fusion results. Still a classical numeric example and a real fault diagnosis application both demonstrate its effectiveness and efficiency, and compared with other current popular methods including two of our previous works, this new approach can converge fast and reduce most uncertainty of decision-making when fusing highly conflicting evidences.

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.


2018 ◽  
Vol 613-614 ◽  
pp. 1024-1030 ◽  
Author(s):  
Cristián González ◽  
Miguel Castillo ◽  
Pablo García-Chevesich ◽  
Juan Barrios

Measurement ◽  
2020 ◽  
Vol 165 ◽  
pp. 108129 ◽  
Author(s):  
Xiancheng Ji ◽  
Yan Ren ◽  
Hesheng Tang ◽  
Chong Shi ◽  
Jiawei Xiang

2014 ◽  
Vol 1030-1032 ◽  
pp. 1764-1768 ◽  
Author(s):  
Wei Xiao Xu ◽  
Ji Wen Tan ◽  
Hong Zhan

Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.


Author(s):  
J. M. MERIGÓ ◽  
M. CASANOVAS ◽  
L. MARTÍNEZ

In this paper, we develop a new approach for decision making with Dempster-Shafer theory of evidence by using linguistic information. We suggest the use of different types of linguistic aggregation operators in the model. We then obtain as a result, the belief structure — linguistic ordered weighted averaging (BS-LOWA), the BS — linguistic hybrid averaging (BS-LHA) and a wide range of particular cases. Some of their main properties are studied. Finally, we provide an illustrative example that shows the different results obtained by using different types of linguistic aggregation operators in the new approach.


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