Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

Infrared short-circuit detection for electrolytic copper refining

Authors
R.M. Jia, X.L. Ma, W.Q. He
Corresponding Author
R.M. Jia
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.113How to use a DOI?
Keywords
infrared images; short-circuit fault; copper electrolytic; pixel ordering PCA; SVM.
Abstract
This paper proposes an automatic detection method for short-circuit fault that based on the thermal radiation principle of infrared image. During copper electrolytic refining, short circuits between cathode and anode plates will lower the production efficiency. It is necessary to detect short circuits timely to reduce the electricity loss. Firstly, the positive and negative samples were collected that came from the infrared images segmentation of the electrolytic tank images. Then, pixel ordering PCA feature extraction algorithm is proposed to obtain the samples feature. Finally, SVM classifier is used to recognize the short circuits. Experiment results prove that the recognition rate based on proposed method is better than other algorithms, and this method has been applied in the electrolytic copper factory.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.113How 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  - R.M. Jia
AU  - X.L. Ma
AU  - W.Q. He
PY  - 2016/11
DA  - 2016/11
TI  - Infrared short-circuit detection for electrolytic copper refining
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.113
DO  - https://doi.org/10.2991/aest-16.2016.113
ID  - Jia2016/11
ER  -