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Conflict evidence management in fault diagnosis

  • Kaijuan Yuan
  • Yong DengEmail author
Original Article
  • 110 Downloads

Abstract

Dempster–Shafer (D–S) theory of evidence is widely used in many real application systems. It can not only deal with imprecise and uncertain information but also combine evidences of different sensors. Therefore it plays an important role in multi-sensor reports’ combination in fault diagnosis. However, when the evidences highly conflict with others, Dempster’s combination rule may lead to a counter-intuitive result and come to a wrong conclusion. It is inevitable to handle conflict in fault diagnosis. This paper proposes a new method to address the issue. Deng entropy function is adopted to measure the information volume of evidences. Evidence distance is introduced to represent the compatibility of evidences. An improved combination method considering both the uncertainty of evidences and the conflict degree of the system is proposed. The proposed method can deal with conflicting evidences efficiently. An application in fault diagnosis is illustrated to show the efficiency of the new method and the result is compared with that of other methods. Besides, and example in IRIS based on information fusion is given to validate the accuracy of the proposed method.

Keywords

Dempster–Shafer evidence theory Fault diagnosis Information fusion Conflict management Deng entropy 

Notes

Acknowledgements

The authors greatly appreciate the reviews’ suggestions and the editor’s encouragement. The work is partially supported by National Natural Science Foundation of China (Grant nos. 61573290,61503237).

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.School of Computer and Information ScienceSouthwest UniversityChongqingChina
  3. 3.Institute of Fundamental and Frontier ScienceUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.Big Data Decision InstituteJinan UniversityGuangzhouChina
  5. 5.Institute of Integrated Automation, School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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