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.

提出了一种基于信息融合的新方法来定义依赖于结合不同信息源的辅助系统，以同时提供评估。本文的主要贡献是提供了一个利用证据理论来融合 n 个信息源的通用框架。融合能够提供更强大的预测和相应的不确定性，可用于评估预测的准确性。此外，我们提供了一种关于信息融合的方法，涉及两个主要来源：基于机器数据的集成分类器和以专家为中心的模型。我们通过使用工业设置的数据演示了信息融合方法，从而完成了该研究的应用部分。此外，我们还提出了一种使用证据理论方法来更新基于数据的模型的方法，以解决数据漂移问题。我们使用 Benchmark Tennessee Eastman 验证了该方法，并对模型更新参数进行了消融研究。