survey

Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

Authors:
Marcel André Hoffmann

Technische Universität Dresden, Germany

Technische Universität Dresden, Germany

0000-0002-1617-1520
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,
Rainer Lasch

Technische Universität Dresden, Germany

Technische Universität Dresden, Germany

0000-0001-6942-3763
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Authors Info & Claims

ACM Computing Surveys Volume 56 Issue 4Article No.: 82pp 1–33https://doi.org/10.1145/3623378

Published:23 October 2023Publication History

ACM Computing Surveys

Abstract

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.

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ACM Computing Surveys Volume 56, Issue 4
April 2024
1026 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613581
Editor:
Albert Zomaya
University of Sydney, Australia
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Publication History
- Published: 23 October 2023
- Online AM: 8 September 2023
- Accepted: 21 August 2023
- Revised: 13 July 2023
- Received: 19 August 2022
Published in csur Volume 56, Issue 4

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Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

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Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

ACM Computing Surveys

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A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines

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