Proceedings of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F677

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 291–296 (2023)

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Abstract. The manufacturing industry relies on continuous optimization to meet quality and safety standards, which is part of the Industry 4.0 concept. Predicting when a specific part of a product will fail to meet these standards is of utmost importance and requires vast amounts of data, which often is collected from variety of sensors, often reffered to as Industrial Internet of Things (IIoT). Using a published dataset from Bosch, that describes the process at every step of production, we aim to train a machine learning model that can accurately predict faults in the manufacturing process. The dataset provides two years of production data across four production lines and 52 stations. Considering that the data generated from each production part includes 4,264 features, we investigate various feature selection and data preprocessing methods. The obtained results exhibit AUC ROC of up to 0.997, which is remarkable and promising even for real-life production use.

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