Information Fusion for Assistance Systems in Production Assessment
This work addresses the need for reliable assessment in industrial production systems, though it appears incremental as it builds on existing evidence theory methods.
The paper tackles the problem of robust prediction and uncertainty assessment in production assistance systems by proposing a general framework for fusing multiple information sources using evidence theory, demonstrating it on industrial data with validation on the Tennessee Eastman benchmark.
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.