Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

Alexander Nikitin*, Samuel Kaski

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationKDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ISBN (Electronic)978-1-4503-9385-0
Publication statusPublished - 14 Aug 2022
MoE publication typeA4 Article in a conference publication
EventACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington, United States
Duration: 14 Aug 202218 Aug 2022
Conference number: 28


ConferenceACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated titleKDD
Country/TerritoryUnited States
Internet address


  • predictive maintenance
  • machine learning
  • Bayesian optimisation
  • data mining
  • human-in-the-loop machine learning


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