NeuroPipe: a neural-network-based automatic pipeline inspection system

Robert Suna; Karsten Berns

doi:10.1117/12.262509

19 December 1996 NeuroPipe: a neural-network-based automatic pipeline inspection system

Robert Suna, Karsten Berns

Author Affiliations +

Proceedings Volume 2911, Advanced Sensor and Control-System Interface; (1996) https://doi.org/10.1117/12.262509
Event: Photonics East '96, 1996, Boston, MA, United States

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.

Citation Download Citation

Robert Suna and Karsten Berns "NeuroPipe: a neural-network-based automatic pipeline inspection system", Proc. SPIE 2911, Advanced Sensor and Control-System Interface, (19 December 1996); https://doi.org/10.1117/12.262509

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