An improved combination rule in fault diagnosis based on dempster shafer theory

Author(s):  
Yong Deng ◽  
Dong Wang ◽  
Qi Li
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.


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.


2012 ◽  
Vol 241-244 ◽  
pp. 405-409 ◽  
Author(s):  
Yan Qin Su ◽  
Ji Hong Cheng ◽  
Ting Xue Xu

There is the advantage of Rough Sets Theory for redundant condition reduction and D-S Theory for combination rules reasoning, one fusion approach based on the two theories was given. Firstly, the test data was discretizated and attribution reduced to get the reduction decision table. Then, the basic probability assignment was gotten through calculating the condition attribution of the decision table while the condition attribution was regarded as evidence input and the decision attribution as discernment frame. Finally, the evidence was combination reasoned and the fault diagnosis results were gotten, and the application example was verified its validity.


2018 ◽  
Vol 25 (1) ◽  
pp. 360-371 ◽  
Author(s):  
Tusongjiang Kari ◽  
Wensheng Gao ◽  
Dongbo Zhao ◽  
Ziwei Zhang ◽  
Wenxiong Mo ◽  
...  

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