Proceedings of the International Symposium on Mechanical Engineering and Material Science

Reliability Prediction for CNC Machine of Spindle System

Authors
Xiaocui Z, Fei C, Jili W, Zhaojun Y, Guofa L, Z Xinge
Corresponding Author
Xiaocui Z
Available Online December 2016.
DOI
https://doi.org/10.2991/ismems-16.2016.62How to use a DOI?
Keywords
CNC machine, Spindle system, Grey theory, Reliability prediction
Abstract

Grey prediction of CNC machine spindle system failure is modeled with GM(1,1) grey prediction as data accumulation weaken randomness and strengthen regularity. The model result provides preventative maintenance of CNC machine spindle system with theoretical foundation. The example analysis shows that grey prediction model can exactly predict time of failure quickly. It is effective to be used in reliability prediction of CNC machine spindle system. The grey prediction model also can predict failure time of CNC machine and the other subsystem

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 International Symposium on Mechanical Engineering and Material Science
Series
Advances in Engineering Research
Publication Date
December 2016
ISBN
10.2991/ismems-16.2016.62
ISSN
2352-5401
DOI
https://doi.org/10.2991/ismems-16.2016.62How 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  - Xiaocui Z
AU  - Fei C
AU  - Jili W
AU  - Zhaojun Y
AU  - Guofa L
AU  - Z Xinge
PY  - 2016/12
DA  - 2016/12
TI  - Reliability Prediction for CNC Machine of Spindle System
BT  - Proceedings of the International Symposium on Mechanical Engineering and Material Science
PB  - Atlantis Press
SP  - 365
EP  - 370
SN  - 2352-5401
UR  - https://doi.org/10.2991/ismems-16.2016.62
DO  - https://doi.org/10.2991/ismems-16.2016.62
ID  - Z2016/12
ER  -