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Authors: Rosaria Rossini 1 ; Nicolò Bertozzi 1 ; Eliseu Pereira 2 ; Claudio Pastrone 1 and Gil Gonçalves 2

Affiliations: 1 LINKS Foundation, Turin, Italy ; 2 SYSTEC, Research Center for Systems and Technologies, Faculty of Engineering, University of Porto, Porto, Portugal

Keyword(s): Predictive Maintenance, Machine Learning, Feature Engineering, Manufacturing, Log Data, Drilling.

Abstract: Machines can generate an enormous amount of data, complemented with production, alerts, failures, and maintenance data, enabling through a feature engineering process the generation of solid datasets. Modern machines incorporate sensors and data processing modules from factories, but in older equipment, these devices must be installed with the machine already in production, or in some cases, it is not possible to install all required sensors. In order to overcome this issue, and quickly start to analyze the machine behavior, in this paper, a two-step log & production-based approach is described and applied to log and production data with the aim of exploiting feature engineering applied to an industrial dataset. In particular, by aggregating production and log data, the proposed two-steps analysis can be applied to predict if, in the near future, I) an error will occur in such machine, and II) the gravity of such error, i.e. have a general evaluation if such issue is a candidate fail ure or a scheduled stop. The proposed approach has been tested on a real scenario with data collected from a woodworking drilling machine. (More)

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Paper citation in several formats:
Rossini, R.; Bertozzi, N.; Pereira, E.; Pastrone, C. and Gonçalves, G. (2022). Feature Extraction and Failure Detection Pipeline Applied to Log-based and Production Data. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-583-8; ISSN 2184-285X, SciTePress, pages 320-327. DOI: 10.5220/0011268700003269

@conference{data22,
author={Rosaria Rossini. and Nicolò Bertozzi. and Eliseu Pereira. and Claudio Pastrone. and Gil Gon\c{C}alves.},
title={Feature Extraction and Failure Detection Pipeline Applied to Log-based and Production Data},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA},
year={2022},
pages={320-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011268700003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA
TI - Feature Extraction and Failure Detection Pipeline Applied to Log-based and Production Data
SN - 978-989-758-583-8
IS - 2184-285X
AU - Rossini, R.
AU - Bertozzi, N.
AU - Pereira, E.
AU - Pastrone, C.
AU - Gonçalves, G.
PY - 2022
SP - 320
EP - 327
DO - 10.5220/0011268700003269
PB - SciTePress