Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

Industry 4.0 - Potentials for Predictive Maintenance

Authors
Zhe Li, Kesheng Wang, Yafei He
Corresponding Author
Zhe Li
Available Online November 2016.
DOI
10.2991/iwama-16.2016.8How to use a DOI?
Keywords
Industry 4.0; industrial big data; predictive maintenance; maintenance management
Abstract

Abstract: Industry 4.0 represents the coming fourth industrial revolution on the way to combine modern industries with Cyber-Physical Systems, Internet of Things and Internet of Services. In an Industry 4.0 factory, machines are connected as a collaborative community to collect, exchange and analyse data systematically. This paper investigates the potentials and trends of predictive maintenance and maintenance management in industrial big data and Cyber-Physical Systems environment. Furthermore, the development of predictive maintenance, its technical challenges, and the potentials under Industry 4.0 era was researched to discover the linkage between Industry 4.0 and predictive maintenance.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation
Series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
10.2991/iwama-16.2016.8How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhe Li
AU  - Kesheng Wang
AU  - Yafei He
PY  - 2016/11
DA  - 2016/11
TI  - Industry 4.0 - Potentials for Predictive Maintenance
BT  - Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SP  - 42
EP  - 46
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.8
DO  - 10.2991/iwama-16.2016.8
ID  - Li2016/11
ER  -