Share Email Print
cover

Proceedings Paper

Identification defect character of MMM signals based on wavelet singular entropy and RBFNN
Author(s): Lan Zhang; Yongrui Zhao; Chong Tian
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Metal magnetic memory is a novel NDT method that can be used to detect residual stress distribution of ferromagnetic components.Wavelet decomposition and entropy theory are used and wavelet singular entropy is introduced to extract characteristic from abnormal signals of defect. Furthermore, RBF neural network is utilized to identify defect character. Experimental results showed that, compared to the traditional gradient value, the proposed new method can be used to effectively reflect defect character and it is immune to the effect of noises.

Paper Details

Date Published: 31 December 2010
PDF: 6 pages
Proc. SPIE 7544, Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 75443G (31 December 2010); doi: 10.1117/12.886053
Show Author Affiliations
Lan Zhang, China Univ. of Petroleum (China)
Yongrui Zhao, China Univ. of Petroleum (China)
Chong Tian, Offshore Oil Engineering (Qingdao) Co., Ltd. (China)


Published in SPIE Proceedings Vol. 7544:
Sixth International Symposium on Precision Engineering Measurements and Instrumentation
Jiubin Tan; Xianfang Wen, Editor(s)

© SPIE. Terms of Use
Back to Top