In order to deal with the generalized uncertainty of the process variable, under the framework of Dempster–Shafer theory of evidence (DST), a data-driven approach without any probabilistic assumption is presented via the dynamic form of the evidence reasoning (ER) rule. Firstly, the process variable is transformed into the corresponding alarm evidence according to referential evidential matrix constructed by casting historical samples. Secondly, the ER rule is proposed to recursively combine the current and historical alarm evidence to generate the global alarm evidence for alarm decision. In the process of recursive fusion, the forgetting strategy is introduced to calculate the reliability factors of the current and historical alarm evidence; the genetic algorithm is designed to optimize the importance weights of evidence. Finally, numerical experiment and industrial case are given to show that the proposed method has a better performance than the classical methods and the initial conditional evidence updating method.

为了处理过程变量的广义不确定性，在 Dempster-Shafer 证据理论 (DST) 的框架下，通过动态形式的证据推理 (ER) 规则，提出了一种没有任何概率假设的数据驱动方法. 首先，根据铸造历史样本构建的参考证据矩阵，将过程变量转化为相应的报警证据。其次，提出ER规则，递归地结合当前和历史报警证据，生成全局报警证据，用于报警决策。在递归融合过程中，引入遗忘策略计算当前和历史告警证据的可靠性因素；遗传算法旨在优化证据的重要性权重。最后，