Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions
It provides incremental insights for Industry 4.0 practitioners on optimizing predictive maintenance with Weibull-distributed data.
This work analyzes two event-driven predictive maintenance approaches—classification and regression—using state-of-the-art solutions, data preprocessing, and algorithms to identify strengths and guide practitioners on impactful aspects.
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.