  Predictive maintenance is used in industrial applications to increase machine
availability and optimize cost related to unplanned maintenance. In most cases,
predictive maintenance applications use output from sensors, recording physical
phenomenons such as temperature or vibration which can be directly linked to
the degradation process of the machine. However, in some applications, outputs
from sensors are not available, and event logs generated by the machine are
used instead. We first study the approaches used in the literature to solve
predictive maintenance problems and present a new public dataset containing the
event logs from 156 machines. After this, we define an evaluation framework for
predictive maintenance systems, which takes into account business constraints,
and conduct experiments to explore suitable solutions, which can serve as
guidelines for future works using this new dataset.
