Abstract
Dempster–Shafer evidence theory is widely used in information fusion. However, it may lead to an unreasonable result when dealing with high conflict evidence. In order to solve this problem, we put forward a new method based on the credibility of evidence. First, a novel belief entropy, Deng entropy, is applied to measure the information volume of the evidence and then the discounting coefficients of each evidence are obtained. Finally, weighted averaging the evidence in the system, the Dempster combination rule was used to realize information fusion. A weighted averaging combination role is presented for multi-sensor data fusion in fault diagnosis. It seems more reasonable than before using the new belief function to determine the weight. A numerical example is given to illustrate that the proposed rule is more effective to perform fault diagnosis than classical evidence theory in fusing multi-symptom domains.
Highlights
With the progress of modern production and the level of science and technology, the structure of modern engineering system is more and more complex.[1]
There is no single sensor that can reliably obtain all the information required for fault diagnosis.[9,10]
We mainly introduce a few concepts used in Dempster–Shafer evidence theory.[20,21]
Summary
With the progress of modern production and the level of science and technology, the structure of modern engineering system is more and more complex.[1]. We present a new weighted average method based on information volume that can increase the accuracy of decision making through fusing multi-source information. Assigning the BPAs to the average and fusing the information by Dempster combination rule Comparing with others, it can deal with conflict and has a fast rate of convergence. With the proportion of Deng entropy as the weight of evidence and the weighted average of multi-source evidence, we can fuse information by Dempster combination rule. This method inherits all the advantages of Murphy method and has a stronger anti-interference ability and faster convergence speed.
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