Diagnosis of Aviation Fault Arc Based on LS-SVM

doi:10.16628/j.cnki.2095-8188.2018.10.008

Abstract

Abstract: The least-squares support vector machine algorithm is applied to detect the aviation fault arc in this paper.The LS-SVM learning machine is constructed at first.The feature vectors extracted from fault arc information are used as the input vectors of least-squares support vector machine.LS-SVM classifier is trained and tested to identify the aviation fault arc for linear load,nonlinear load and unknown load.The results show that the algorithm can effectively identify whether arc occurs,but the judgement of specific types of fault arc still needs to be improved.

Key words: least-squares support vector machine(LS-SVM), aviation fault arc, fault arc diagnosis, feature vectors

CLC Number:

TM501⁺.2

LI Lansong, ZHOU Yue, XIONG Xiang, YU Guanghui, WANG Yongxing. Diagnosis of Aviation Fault Arc Based on LS-SVM[J]. DIANQI YU NENGXIAO GUANLI JISHU, 2018, 0(10): 45-49.

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