scholarly journals Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Fault Diagnosis ◽  
Conflict Management ◽  
Information Fusion ◽  
Uncertainty Measure ◽  
Combination Rule ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Weight Value ◽  
Shafer Theory

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.

scholarly journals A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Medical Diagnosis ◽  
Real Life ◽  
Entropy Function ◽  
New Method ◽  
Combination Rule ◽  
Initial Weight ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

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.

scholarly journals A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1061
Author(s):  
Yu Zhang ◽  
Fanghui Huang ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Maximum Entropy ◽  
Information Fusion ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Numerical Examples ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Open Issue ◽  
Shafer Theory ◽  
Fusion Framework

The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.

Fault Diagnosis of Engine Based on Improved Dempster-Shafer Information Fusion Method

2009 ◽  
Vol 16-19 ◽  
pp. 1310-1317
Author(s):  
Wei Zhou ◽  
Ying Ji Liu ◽  
Qing Fu Cao ◽  
Tian Xia Zhang
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
New Combination ◽  
Fusion Technology ◽  
Combination Rule ◽  
Experiment Research ◽  
New Approach ◽  
Weight Value

In order to enhance the accuracy of engine fault diagnosis, information fusion technology was applied and a novel combination method is proposed based on D-S evidence theory. The evidence groups were classified by evidence conflict coefficient, the importance of each highly conflict evidence was calculated, and the credibility of each evidence was determined with a distance function of evidence bodies. Then the weight value of each evidence was revised with its importance and credibility respectively. Finally, the Dempster combination rule was used to realize the information fusion. The effectiveness of the new approach proposed was verified by theoretical analysis and experiment research results. Comparing with D-S evidence theory and the improved synthesis formula, the new combination method is more efficient in improving the accuracy and the certainty degree of engine fault diagnosis.

Modified Interval Dempster-Shafer Theory and its Application for Target Identification

2012 ◽  
Vol 190-191 ◽  
pp. 1153-1156
Author(s):  
Hai Jun Liu ◽  
Hong Shan Nie ◽  
Hong Qi Yu ◽  
Hong Hu Hua ◽  
Zheng Liu
Keyword(s):  
Measurement Uncertainty ◽  
Target Identification ◽  
Identification Algorithm ◽  
Combination Rules ◽  
Feature Measurement ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

To deal with the problem of target identification caused by the achieved reliability of multi sensors and the feature measurement uncertainty of the target, this paper proposes a new identification algorithm based on Modified Interval Dempster-Shafer Theory (MIDST), which models sensor’s reliability as scalar value and identification outputs of each sensor as interval values, and then combines the actual interval outputs through interval evidence combination rules. At last, one simulation is presented to demonstrate the identification capability of the MIDST algorithm.

An intelligent fault diagnosis approach based on Dempster-Shafer theory for hydraulic valves

Measurement ◽  
2020 ◽  
Vol 165 ◽  
pp. 108129 ◽  
Author(s):  
Xiancheng Ji ◽  
Yan Ren ◽  
Hesheng Tang ◽  
Chong Shi ◽  
Jiawei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Hydraulic Valves ◽  
Diagnosis Approach
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