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

Authors

  • Yukun Dong Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China, Chengdu, 610054, China
  • Jiantao Zhang College of Information Science and Technology Jinan University, Tianhe, Guangzhou, 510632, China
  • Zhen Li College of Information Science and Technology Jinan University, Tianhe, Guangzhou, 510632, China
  • Yong Hu Big Data Decision Institute Jinan University, Tianhe, Guangzhou, 510632, China
  • Yong Deng 1. Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China, Chengdu, 610054, China 2. Big Data Decision Institute Jinan University, Tianhe, Guangzhou, 510632, China

Keywords:

Dempster-Shafer evidence theory, sensor data fusion, fault diagnosis, evidence distance, belief entropy, information volume

Abstract

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.

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Published

2019-05-31

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