Feature Extraction and Failure Detection Pipeline Applied to Log-based and Production Data

Rosaria Rossini, Nicolò Bertozzi, Eliseu Pereira, Claudio Pastrone, Gil Gonçalves

2022

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 failure or a scheduled stop. The proposed approach has been tested on a real scenario with data collected from a woodworking drilling machine.

Paper Citation

in Harvard Style

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 - Volume 1: DATA, ISBN 978-989-758-583-8, pages 320-327. DOI: 10.5220/0011268700003269

in Bibtex Style

@conference{data22,
author={Rosaria Rossini and Nicolò Bertozzi and Eliseu Pereira and Claudio Pastrone and Gil Gonçalves},
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 - Volume 1: DATA,},
year={2022},
pages={320-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011268700003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Feature Extraction and Failure Detection Pipeline Applied to Log-based and Production Data
SN - 978-989-758-583-8
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