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This work explores two approaches to event-driven predictive maintenance inIndustry 4.0 that cast the problem at hand as a classification or a regressionone, respectively, using as a starting point two state-of-the-art solutions.For each of the two approaches, we examine different data preprocessingtechniques, different prediction algorithms and the impact of ensemble andsampling methods. Through systematic experiments regarding the aspectsmentionedabove,we aimto understand the strengths of the alternatives, and moreimportantly, shed light on how to navigate through the vast number of suchalternatives in an informed manner. Our work constitutes a key step towardsunderstanding the true potential of this type of data-driven predictivemaintenance as of to date, and assist practitioners in focusing on the aspectsthat have the greatest impact.

Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions