  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.
