Application of neural network in metal loss evaluation for gas conducting pipelines

Authors:
Wei Zhang;Jing-Tao Guo;Song-Ling Huang
Affiliations:
Department of Mechanical Engineering, Tsinghua University, Beijing, China;Department of Mechanical Engineering, Tsinghua University, Beijing, China;Department of Electrical Engineering, Tsinghua University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

An application based on BP neural network is conducted on evaluating the state of metal loss in gas conducting pipelines. We employ network to quantify the defects of metal loss in outer pipe spool according to the wm, lm and hm of MFL (magnetic flux leakage), which are the width, length and depth of the area of abnormal magnetic data. After quantifying, we obtain w, l and d, which are deemed to the width, length and depth of the defect. The data to train our networks are developed from MFL data which are picked by Pipe Pig moving in service pipe with a constant velocity parallel to pipe axis.