Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

The improved particle swarm breaker fault status parameter optimization of SVM classification

Authors
Yihang Sun
Corresponding Author
Yihang Sun
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.195How to use a DOI?
Keywords
circuit breaker;SVM;vibration signal;PSO;energy method;fault diagnosis
Abstract

In order to improve the mechanical structure of the type of fault resolution precision high voltage circuit breaker spring mechanism, the paper analyzes the characteristics of the circuit breaker and the combination of mechanical vibration signal PSO algorithm (PSO) SVM parameter optimization method proposed collaborative dynamic acceleration constant inertia weight particle swarm optimization (WCPSO) optimization support vector machine (SVM) analysis breaker fault classification parameters and kernel function parameters. The vibration signal circuit breaker empirical mode decomposition, the total intrinsic mode components through energy analysis to obtain the required fault feature vectors and support vector machine as input, the use of dynamic acceleration constant synergy inertia weight PSO support vector machines penalty factor C and radial basis kernel function parameters optimize the fault feature vector signal input test samples after SVM training sample trained optimized for fault classification, fault status classification. The experimental analysis of this method can effectively improve the resolution of the breaker failure signal type Accuracy.

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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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
978-94-6252-285-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmita-16.2016.195How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Yihang Sun
PY  - 2017/01
DA  - 2017/01
TI  - The improved particle swarm breaker fault status parameter optimization of SVM classification
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.195
DO  - https://doi.org/10.2991/icmmita-16.2016.195
ID  - Sun2017/01
ER  -