Dempster–Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this article, we propose a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigate the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also present several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we apply the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verifies the practicability and effectiveness of our proposed novel measurement.

中文翻译：

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一种新的决策冲突度量及其在故障诊断中的应用

Dempster-Shafer 证据 (DSE) 理论允许组合来自不同数据源的证据片段以推导出作为一种模糊度量类型的置信度函数，是用于不确定性推理的通用框架。在这个框架中，如何最佳地管理 DSE 中多条证据的冲突仍然是支持决策的一个悬而未决的问题。现有的冲突度量方法可以实现可接受的结果，但没有充分考虑使用新颖的冲突度量在决策层面进行优化。在本文中，我们通过测量决策中两个证据之间的冲突，为信念函数提出了一种新的证据相关系数 (ECC)。然后，我们研究了我们提出的证据相关性和冲突系数的特性，这些都被证明满足冲突测量的理想属性，包括非负性、对称性、有界性、极端一致性和对细化不敏感。我们还提供了几个示例和比较，以证明我们提出的 ECC 方法的优越性。最后，我们将提出的 ECC 应用于电机转子故障诊断的决策应用，验证了我们提出的新型测量方法的实用性和有效性。