Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

Research output: Contribution to journalResearch articleContributedpeer-review


The application of Artificial Intelligence (AI) approaches in industrial maintenance for fault detection and prediction has gained much attention from scholars and practitioners. This survey systematically assesses and classifies the state-of-the-art algorithms applied to data-driven maintenance in recent literature. The taxonomy provides a so far not existing overview and decision aid for research and practice regarding suitable AI approaches for each maintenance application. Moreover, we consider trends and further research demand in this area. Finally, a newly developed holistic maintenance framework contributes to a practice-oriented implementation of AI and considers crucial managerial aspects of an efficient maintenance system.


Original languageEnglish
Article number82
Pages (from-to)1–33
Number of pages33
JournalACM Computing Surveys
Issue number4
Publication statusPublished - 23 Oct 2023

External IDs

ORCID /0000-0001-6942-3763/work/142252952
ORCID /0000-0002-1617-1520/work/142254885
Mendeley ba42d14f-1dfe-3e1f-b771-2b9280af86ce
unpaywall 10.1145/3623378
Scopus 85179134269



  • Artificial Intelligence, condition monitoring, machine learning, Predictive maintenance, prescriptive maintenance, RUL

Library keywords