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Intelligent Systems and Applications pp 1–18Cite as

A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 650))

Abstract

The defect assessment process in oil and gas pipelines consists of three stages: defect detection, defect dimension (i.e., defect depth and length) prediction, and defect severity level determination. In this paper, we propose an intelligent system approach for defect prediction in oil and gas pipelines. The proposed technique is based on the magnetic flux leakage (MFL) technology widely used in pipeline monitoring systems. In the first stage, the MFL signals are analyzed using the Wavelet transform technique to detect any metal-loss defect in the targeted pipeline. In case of defect existence, an adaptive neuro-fuzzy inference system is utilized to predict the defect depth. Depth-related features are first extracted from the MFL signals, and then used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. To further improve the accuracy of the defect depth, predicted by the proposed model, highly-discriminant features are then selected by using the weight-based support vector machine (SVM). Experimental work shows that the proposed technique yields promising results, compared with those achieved by some service providers.

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Acknowledgments

This work was made possible by NPRP grant # [5-813-1-134] from Qatar Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Information Systems Department, Ahmed Bin Mohammed Military College, Doha, Qatar

    Abduljalil Mohamed & Mohamed Salah Hamdi

  2. Electrical and Computer Engineering Department, Concordia University, Montreal, QC, Canada

    Sofiène Tahar

Authors
  1. Abduljalil Mohamed
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  2. Mohamed Salah Hamdi
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  3. Sofiène Tahar
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Corresponding author

Correspondence to Abduljalil Mohamed .

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Editors and Affiliations

  1. Faculty of Computing and Engineering, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Mohamed, A., Hamdi, M.S., Tahar, S. (2016). A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-33386-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33384-7

  • Online ISBN: 978-3-319-33386-1

  • eBook Packages: EngineeringEngineering (R0)

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