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

Intelligent Predictive Maintenance (IPdM) for Elevator Service- Through CPS, IOT&S and Data Mining

Authors
Kesheng Wang, Guohong Dai, Lanzhong Guo
Corresponding Author
Kesheng Wang
Available Online November 2016.
DOI
https://doi.org/10.2991/iwama-16.2016.1How to use a DOI?
Keywords
Industry 4.0; CPS; IOT&S; Big data/ Data Mining; Elevator service; Smart elevator
Abstract
With the rapid economic growth and urbanization development, most of elevator production and service companies have completely changed their service policy opting to eliminate the standard preventive service policy. They now provide some form of predictive service policy and emphasize that they utilize remote monitoring of elevators to detect faults and estimate when components may need to be maintained due to actual usage. However, most of them are not true predictive maintenance policy. They merely a preventive, slightly enhanced, usage-based program. What is the future of elevator production and service companies? This paper will challenge the word "predictive maintenance" and present the framework of intelligent predictive maintenance system for smart elevator service based on industry 4.0 concepts.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kesheng Wang
AU  - Guohong Dai
AU  - Lanzhong Guo
PY  - 2016/11
DA  - 2016/11
TI  - Intelligent Predictive Maintenance (IPdM) for Elevator Service- Through CPS, IOT&S and Data Mining
BT  - 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.1
DO  - https://doi.org/10.2991/iwama-16.2016.1
ID  - Wang2016/11
ER  -