Proceedings of the 4th Annual International Conference on Material Engineering and Application (ICMEA 2017)

Rough Sets and Genetic Algorithm for Power Cable Joints Fault Diagnosis

Authors
Xin Li, Hongyi Zhou, Pan Dai, Zhihong Yang, Yan Chen, Yuanxin Chen
Corresponding Author
Xin Li
Available Online February 2018.
DOI
10.2991/icmea-17.2018.7How to use a DOI?
Keywords
rough set; cable jonit; genetic algorithm; fault diagnosis
Abstract

The influence factors of cable junction accidents was determined by accidents analysis. The research methods to utilize rough set theory to extract effective information from cable junction accident data in history was discussed in this paper. Cable junction accident decision table attribute reduction algorithm and cable junction accident decision table based on genetic algorithm were obtained. The results show that the model is scientific and reasonable, and the algorithm is efficient and feasible.

Copyright
© 2018, 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 4th Annual International Conference on Material Engineering and Application (ICMEA 2017)
Series
Advances in Engineering Research
Publication Date
February 2018
ISBN
10.2991/icmea-17.2018.7
ISSN
2352-5401
DOI
10.2991/icmea-17.2018.7How to use a DOI?
Copyright
© 2018, 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  - Xin Li
AU  - Hongyi Zhou
AU  - Pan Dai
AU  - Zhihong Yang
AU  - Yan Chen
AU  - Yuanxin Chen
PY  - 2018/02
DA  - 2018/02
TI  - Rough Sets and Genetic Algorithm for Power Cable Joints Fault Diagnosis
BT  - Proceedings of the 4th Annual International Conference on Material Engineering and Application (ICMEA 2017)
PB  - Atlantis Press
SP  - 27
EP  - 30
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmea-17.2018.7
DO  - 10.2991/icmea-17.2018.7
ID  - Li2018/02
ER  -