The improved particle swarm breaker fault status parameter optimization of SVM classification
- https://doi.org/10.2991/icmmita-16.2016.195How to use a DOI?
- circuit breaker;SVM;vibration signal;PSO;energy method;fault diagnosis
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.
- © 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 -