scholarly journals iDCR: Improved Dempster Combination Rule for multisensor fault diagnosis

2021 ◽  
Vol 104 ◽  
pp. 104369
Author(s):  
Nimisha Ghosh ◽  
Sayantan Saha ◽  
Rourab Paul
Keyword(s):  
Fault Diagnosis ◽  
Combination Rule

scholarly journals Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

2019 ◽  
Vol 14 (3) ◽  
pp. 329-343 ◽  
Author(s):  
Yukun Dong ◽  
Jiantao Zhang ◽  
Zhen Li ◽  
Yong Hu ◽  
Yong Deng
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Distance Function ◽  
Evidence Theory ◽  
Sensor Data ◽  
Combination Rule ◽  
Sensor Data Fusion ◽  
Real Application ◽  
Belief Entropy

Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment

2013 ◽  
Vol 341-342 ◽  
pp. 715-718
Author(s):  
Jin Luo ◽  
Qi Bin Deng
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fuzzy Set ◽  
Fuzzy Theory ◽  
Electronic Equipment ◽  
Uncertain Information ◽  
Combination Rule ◽  
Fault Features ◽  
Diagnosis Method ◽  
Fuzzy Target

Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves fault identifications, and the fusion diagnosis method based on multi fuzzy evidence matching enhances the precision and reliability of the system fault diagnosis decision furthermore.

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.

A Robust Data-Driven Fault Diagnosis scheme based on Recursive Dempster–Shafer Combination Rule*

2021 ◽  
Author(s):  
N. Cartocci ◽  
M. R. Napolitano ◽  
G. Costante ◽  
F. Crocetti ◽  
P. Valigi ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Combination Rule

Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory

2012 ◽  
Vol 241-244 ◽  
pp. 288-292
Author(s):  
Zhi Song Wang ◽  
Li Wei Tang ◽  
Wen Wen Yu ◽  
Jin Hua Cao
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Evidence Theory ◽  
Basic Belief ◽  
Combination Rule ◽  
Fusion Model ◽  
Minkowski Distance ◽  
Model Based ◽  
Feature Parameters ◽  
Diagnosis Method

Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.

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.

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