We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

翻译：我们提出了一种新颖的方法来定义依赖信息融合的辅助系统，该系统在提供评估结果的同时整合不同信息源。本文的主要贡献在于利用证据理论构建了一个通用框架，用于融合n个信息源。该融合过程能生成更稳健的预测结果，并附带相关的不确定性度量，可用于评估预测的可信度。此外，我们针对两类主要信息源提出了具体融合方法：基于机器数据的集成分类器与专家中心模型。通过工业场景数据验证了该信息融合方法，由此构成了本研究的应用部分。同时，我们提出采用证据理论方法更新数据驱动模型，以解决数据漂移问题。通过田纳西-伊斯曼基准测试平台进行模型更新参数的消融研究，验证了该方法的有效性。