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.
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