Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoKDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KustantajaACM
Sivut3682–3690
ISBN (elektroninen)978-1-4503-9385-0
DOI - pysyväislinkit
TilaJulkaistu - 14 elok. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington, Yhdysvallat
Kesto: 14 elok. 202218 elok. 2022
Konferenssinumero: 28
https://kdd.org/kdd2022/

Conference

ConferenceACM SIGKDD International Conference on Knowledge Discovery and Data Mining
LyhennettäKDD
Maa/AlueYhdysvallat
KaupunkiWashington
Ajanjakso14/08/202218/08/2022
www-osoite

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