A novel signal processing and defect recognition method based on multi-sensor inspection system

Author

Jin, Tao ; Que, Peiwen

Author_Institution

Dept. of Electr. Eng., Fuzhou Univ., Fuzhou, China

Volume

2

fYear

2010

fDate

12-13 June 2010

Firstpage

435

Lastpage

438

Abstract

This article presented a novel signal processing and defect recognition method in MFL inspection system. During the preprocessing course, time-frequency analysis, median and adaptive filter, and interpolation processing are adopted to preprocess MFL inspection signal. In order to obtain high sensitivity and precision, we adopted multi-sensor data fusion technique to inspection data. A wavelet basis function (WBF) neural network was used to recognize defect parameters. Through constructing a knowledge-based off-line inspection expert system, the system improved its defect recognition capability greatly.

Keywords

adaptive filters; expert systems; inspection; interpolation; knowledge based systems; neural nets; sensor fusion; wavelet transforms; MFL inspection system; adaptive filter; defect recognition method; interpolation processing; knowledge based offline inspection expert system; multisensor data fusion technique; multisensor inspection system; neural network; signal processing; time frequency analysis; wavelet basis function; Artificial neural networks; Control systems; Pipelines; Data fusion; Sensors system; Signal processing; Wavelet basis function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6944-4

Type

conf

DOI

10.1109/CCTAE.2010.5543693

Filename

5543693