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	Quantitative interpretation for the magnetic flux leakage testing data based on neural network
	Song Xiaochun ; Huang Songling ; Zhao Wei
	2010-05-10 ; 2010-05-10 ; OCT 15-17, 2006
会议名称	1st International Symposium on Digital Manufacture, Vols 1-3 ; 1st International Symposium on Digital Manufacture ; Wuhan, PEOPLES R CHINA ; Web of Science
关键词	neural networks magnetic flux leakage (MFL) quantitative interpretation nondestructive testing ALGORITHMS Engineering, Manufacturing Engineering, Mechanical
中文摘要	In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network. Because the wavelet basis function neural network (WBFJNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively, a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes. And such details as the choice of wavelet basis function I the initialization of the weight value and the input normalization are analyzed, the training and testing algorithm for the network are also studied. The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.
会议录出版者	WUHAN UNIV TECHNOLOGY PRESS ; WUHAN ; 122 LUOSHI RD, WUHAN 430070, PEOPLES R CHINA
语种	英语 ; 英语
内容类型	会议论文
源URL	[http://hdl.handle.net/123456789/18597]
专题	清华大学
推荐引用方式 GB/T 7714	Song Xiaochun,Huang Songling,Zhao Wei. Quantitative interpretation for the magnetic flux leakage testing data based on neural network[C]. 见:1st International Symposium on Digital Manufacture, Vols 1-3, 1st International Symposium on Digital Manufacture, Wuhan, PEOPLES R CHINA, Web of Science.

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