Research on glass processing under manufacturing cutting machine fault prediction in cloud manufacturing

DOI

10.2991/icmmcce-17.2017.120How to use a DOI?

Keywords

Cutting machine; Fault prediction; Cloud manufacturing; Aproiri; Association rules; Data mining.

Abstract

The prediction of equipment malfunction is crucial to guarantee its safe operation and improve its management efficiency. Combining with the character of cloud manufacturing, this paper makes the malfunction prediction of the cutting machine come true, which is based on the improved Apriori algorithm. First, with the malfunction analysis of the cutting machine, we can set up the comparison table of malfunction features and that of parameter, integrate different manufacturer and data of different equipment into virtual cloud pool and store them in HDFS form. Then, we can explore the association rules of the machine malfunction data when we use Hadoop parallelisation to improve Apriori algorithm and add the temporal concepts. So that we can predict the trend of cutting machine malfunction and support the subsequent health management.

Copyright

© 2017, 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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TY  - CONF
AU  - Hongxia Cai
AU  - Zhuangyu Wei
AU  - Minshan Ren
PY  - 2017/09
DA  - 2017/09
TI  - Research on glass processing under manufacturing cutting machine fault prediction in cloud manufacturing
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 658
EP  - 663
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.120
DO  - 10.2991/icmmcce-17.2017.120
ID  - Cai2017/09
ER  -