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

arXiv: https://arxiv.org/abs/2101.07033 · HTML: https://mlsanity.com/paper/2101.07033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Adaptive Detection of Complex Event Patterns](https://mlsanity.com/paper/1801.08588/) (2018)
- [ONCE and ONCE+: Counting the Frequency of Time-constrained Serial Episodes in a Streaming Sequence](https://mlsanity.com/paper/1801.09639/) (2018)

## Abstract (arXiv)

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.