Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

Maintenance Decision for the Key Parts of Machine Tool Using the Monitoring Information

Authors
Shuang Zhou, Tao Zan, Min Wang, Congcong Guo
Corresponding Author
Shuang Zhou
Available Online July 2016.
DOI
https://doi.org/10.2991/mcae-16.2016.18How to use a DOI?
Keywords
monitoring information; kurtosis value; weibull proportional hazards models; maintenance decision
Abstract
Considering the monitoring information of the operation process of the machine tools, a maintenance decision model is established in this paper. Firstly, the monitoring information of the key parts of the machine tools is obtained by using data acquisition system. Based on the kurtosis value which are extracted from the monitoring information, the weibull proportional hazard model (WPHM) are proposed. In the model, the weibull distribution is regarded as the failure rate function. Then, the maintenance decision model can be obtained based on WPHM. Finally, according to the failure threshold in the decision model, the maintenance decision can be conducted. The results show that this maintenance decision model can not only improve the availability of the key parts greatly, but also make full use of the effective life of machine tools.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering
Series
Advances in Engineering Research
Publication Date
July 2016
ISBN
978-94-6252-237-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mcae-16.2016.18How 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  - Shuang Zhou
AU  - Tao Zan
AU  - Min Wang
AU  - Congcong Guo
PY  - 2016/07
DA  - 2016/07
TI  - Maintenance Decision for the Key Parts of Machine Tool Using the Monitoring Information
BT  - Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering
PB  - Atlantis Press
SP  - 70
EP  - 74
SN  - 2352-5401
UR  - https://doi.org/10.2991/mcae-16.2016.18
DO  - https://doi.org/10.2991/mcae-16.2016.18
ID  - Zhou2016/07
ER  -