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

: 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 de-vices 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.