# Information Fusion for Assistance Systems in Production Assessment

arXiv: https://arxiv.org/abs/2309.00157 · HTML: https://mlsanity.com/paper/2309.00157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Adaptive Embedding Considering Incremental Class](https://mlsanity.com/paper/2008.13351/) (2020)
- [Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application](https://mlsanity.com/paper/2004.13410/) (2020)
- [Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation](https://mlsanity.com/paper/2209.02369/) (2022)
- [Production Assessment using a Knowledge Transfer Framework and Evidence Theory](https://mlsanity.com/paper/2207.03611/) (2022)
- [Anomaly Detection using Ensemble Classification and Evidence Theory](https://mlsanity.com/paper/2212.12092/) (2022)
- [Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems](https://mlsanity.com/paper/2107.11095/) (2021)

## Abstract (arXiv)

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.