Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
This addresses maintenance scheduling for organizations with large workstation fleets, but it is incremental as it builds on existing human-in-the-loop and active learning concepts.
The paper tackles predictive maintenance for workstations by proposing a human-in-the-loop machine learning system that incorporates expert feedback beyond labeling, achieving deployment on thousands of workstations across dozens of companies.
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.