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Fault classification in the process industry using polygon generation and deep learning

Author

Listed:
  • Mohamed Elhefnawy

    (Polytechnique Montréal
    CanmetENERGY)

  • Ahmed Ragab

    (CanmetENERGY
    Menoufia University)

  • Mohamed-Salah Ouali

    (Polytechnique Montréal)

Abstract

This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate “end-to-end learning” in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers.

Suggested Citation

  • Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2022. "Fault classification in the process industry using polygon generation and deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1531-1544, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-021-01742-x
    DOI: 10.1007/s10845-021-01742-x
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    References listed on IDEAS

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    1. Ahmed Ragab & Soumaya Yacout & Mohamed-Salah Ouali & Hany Osman, 2019. "Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 255-274, January.
    2. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    3. Talbot, David & Boiral, Olivier, 2013. "Can we trust corporates GHG inventories? An investigation among Canada's large final emitters," Energy Policy, Elsevier, vol. 63(C), pages 1075-1085.
    4. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
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