  This work explores two approaches to event-driven predictive maintenance in
Industry 4.0 that cast the problem at hand as a classification or a regression
one, respectively, using as a starting point two state-of-the-art solutions.
For each of the two approaches, we examine different data preprocessing
techniques, different prediction algorithms and the impact of ensemble and
sampling methods. Through systematic experiments regarding the aspectsmentioned
above,we aimto understand the strengths of the alternatives, and more
importantly, shed light on how to navigate through the vast number of such
alternatives in an informed manner. Our work constitutes a key step towards
understanding the true potential of this type of data-driven predictive
maintenance as of to date, and assist practitioners in focusing on the aspects
that have the greatest impact.
