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Proceedings Paper

NeuroPipe: a neural-network-based automatic pipeline inspection system
Author(s): Robert Suna; Karsten Berns
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Paper Abstract

Oil- and gas-pipelines must be examined in regular intervals for defects like metal loss. For this reason the Pipetronix company has developed different probes which collect a high number of ultrasonic readings of the wall condition. Based on this measurement the research center for computer science has implemented the automatic inspection system NeuroPipe. The kernel of this inspection tool is a hybrid neural classifier which was trained using manually collected defect examples. The following paper focuses on the aspects of the successful use neural network learning technology for this industrial application. Furthermore the difficulties, when applying these techniques, are discussed.

Paper Details

Date Published: 19 December 1996
PDF: 10 pages
Proc. SPIE 2911, Advanced Sensor and Control-System Interface, (19 December 1996); doi: 10.1117/12.262509
Show Author Affiliations
Robert Suna, Univ. Karlsruhe (Germany)
Karsten Berns, Univ. Karlsruhe (Germany)

Published in SPIE Proceedings Vol. 2911:
Advanced Sensor and Control-System Interface
Bartholomew O. Nnaji, Editor(s)

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