  Predictive maintenance (PdM) is the task of scheduling maintenance operations
based on a statistical analysis of the system's condition. We propose a
human-in-the-loop PdM approach in which a machine learning system predicts
future problems in sets of workstations (computers, laptops, and servers). Our
system interacts with domain experts to improve predictions and elicit their
knowledge. In our approach, domain experts are included in the loop not only as
providers of correct labels, as in traditional active learning, but as a source
of explicit decision rule feedback. The system is automated and designed to be
easily extended to novel domains, such as maintaining workstations of several
organizations. In addition, we develop a simulator for reproducible experiments
in a controlled environment and deploy the system in a large-scale case of
real-life workstations PdM with thousands of workstations for dozens of
companies.
