Abstract
Missing Data is of concern in smart manufacturing. There are various reasons why data may be missing. Buffering issues, sensor failure, or protocol issues can cause missing entries or skipped captures. This missing data leads to distorted datasets, failure to train models, and either to disregard, deletion of records or typically to vast manual re-work efforts. This paper shows types of missing data and various approaches to detect them. Replacement or fill-in approaches are presented and their limitations are highlighted. A more precise imputation method beyond the state of the art is explained in detail. The implementation of this physics-informed imputation method was performed and experiments were carried out. The results of the experiments are presented in this paper and the results discussed.
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