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Dempster–Shafer evidence theory, which is an extension of Bayesian probability theory, is a useful approach to realize multisensor data fusion. It uses mass functions to represent uncertainty, which can produce a satisfactory fusion result. However, when the evidence is highly conflicting, using Dempster–Shafer evidence theory fusion rule to combine the evidence will generate the result contrary to common sense. To solve this issue, we propose a new method for conflict management based on Renyi divergence (RD). Then, by combining RD with the mass function, we develop Renyi‐Belief divergence (RBD). To expand its utility, we modify it and define the modified Renyi‐Belief divergence (MRBD). Our method MRBD integrates the characteristics of mass functions and can handle conflict by measuring the differences between mass functions. Experiments show that MRBD can effectively deal with conflicts. After dealing with the conflicting evidence, we realize multisensor data fusion based on the Dempster–Shafer combination rule. Moreover, we also consider the information quality and belief entropy to reinforce the credibility of evidence. A large number of examples show that the proposed method is feasible and efficient. Finally, in the application of fault diagnosis, our method can effectively determine the fault type.

The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM) problem, it is also very difficult to measure the ambiguity degree of a set of PFN. A new entropy of PFN is proposed based on a technique for order of preference by similarity to ideal solution (Topsis) method of revised relative closeness index in this paper. To verify the new entropy with a good performance in uncertainty measure, a new Pythagorean fuzzy number negation approach is proposed. We develop the PFN negation and find the correlation of the uncertainty measure. Existing methods can only evaluate the ambiguity of a single PFN. The newly proposed method is suitable to systematically evaluate the uncertainty of PFN in Topsis. Nowadays, there are no uniform criteria for measuring service quality. It brings challenges to the future development of airlines. Therefore, grasping the future market trends leads to winning with advanced and high-quality services. Afterward, the applicability in the service supplier selection system with the new entropy is discussed to evaluate the service quality and measure uncertainty. Finally, the new PFN entropy is verified with a good ability in the last MCDM numerical example.

Dempster rule is commonly used to combine evidence from different sensors because of its excellent mathematical properties (commutativity and associativity). However, the conflict coefficient k of this rule cannot be reasonably and effectively express the conflict between evidence, which makes the Dempster rule greatly questioned. To describe in the relationship between evidence (conflict or correlation) more accurately, an evidence correlation coefficient based on generalized information quality is proposed. First of all, according to Deng entropy to measure the uncertainty of each evidence, and combined with the information quality of Yager, new generalized information quality is proposed, which performs well in measuring the basic probability assignment's certainty. Second, the evidence itself is modified by generalized information quality, and the evidence correlation coefficient is calculated based on the Pearson coefficient formula. A new measurement method of evidence conflict based on evidence correlation coefficient is proposed. Finally, combined with the evidence correlation coefficient and DEMATEL model, the evidence is discounted and combined. Numerous examples are used to analyze and compare the evidence conflict coefficient and the evidence combination results. The experimental results demonstrate that, compared with other evidence conflict measurement methods, the evidence conflict coefficient calculated by this method can reflect the difference between evidence more effectively. The result of evidence combination is more reasonable and accurate.

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