SVM classifier for wire extension monitoring using arc sound signal in GMAW

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	SVM classifier for wire extension monitoring using arc sound signal in GMAW
	Ma, Yue-Zhou; Qu, Min; Chen, Jian-Hong
刊名	Hanjie Xuebao/Transactions of the China Welding Institution
	2006-05-01
卷号	27 期号:5 页码:21-26
关键词	Data acquisition Inference engines Monitoring Pattern recognition Quality control Linear prediction coding model Support vector machine (SVM) Welding arc sound Welding parameter monitoring
ISSN号	0253360X
英文摘要	To find the approach of monitoring welding quality by arc sound, the frequency spectral characteristics of the arc sound signals in short circuit GMAW process were analyzed. The concept of tone channel of welding arc was introduced, which was considered a time dependent distributed parameters system influenced by welding parameters, arc behavior and the other factors. The LPC (linear prediction coding) model of the arc sound signal was an estimation of transmission properties of the tone channel. The spectrum analyses indicated that the frequency characteristics of the arc sound signal were closely related to the wire extension, but the correlation presented high complexity and nonlinearity. The classifiers based on SVM (support vector machine) for monitoring wire extensions were established, in which the input vectors of sample sets were built with the predictor coefficients and reflection coefficients of LPC model of the arc sound signals. The training and testing results showed that the SVM classifiers with different kernels are all capable of classifying the wire extension, whose performances were obviously better than that of the RBF (radial basis function) neural networks under the same condition, and present good capability of generalization at small sample set. The classifiers with 3rd order polynomial kernel trained with reflection coefficients input vectors has the best accuracy, i.e. over 98%. The study indicated that forming characteristic vectors by the LPC coefficients of arc sound to build SVM pattern recognition model is a feasible way for welding parameters monitoring.
语种	中文
出版者	Harbin Research Institute of Welding
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/111963]
专题	兰州理工大学
作者单位	Key Laboratory of Non-ferrous Metal Alloys, Lanzhou University of Technology, Lanzhou 730050, China
推荐引用方式 GB/T 7714	Ma, Yue-Zhou,Qu, Min,Chen, Jian-Hong. SVM classifier for wire extension monitoring using arc sound signal in GMAW[J]. Hanjie Xuebao/Transactions of the China Welding Institution,2006,27(5):21-26.
APA	Ma, Yue-Zhou,Qu, Min,&Chen, Jian-Hong.(2006).SVM classifier for wire extension monitoring using arc sound signal in GMAW.Hanjie Xuebao/Transactions of the China Welding Institution,27(5),21-26.
MLA	Ma, Yue-Zhou,et al."SVM classifier for wire extension monitoring using arc sound signal in GMAW".Hanjie Xuebao/Transactions of the China Welding Institution 27.5(2006):21-26.