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An approach to robust fault diagnosis in mechanical systems using computational intelligence

Author

Listed:
  • Adrián Rodríguez Ramos

    (Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE))

  • José M. Bernal de Lázaro

    (Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE))

  • Alberto Prieto-Moreno

    (Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE))

  • Antônio José Silva Neto

    (Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ))

  • Orestes Llanes-Santiago

    (Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE))

Abstract

In this paper a novel approach to design robust fault diagnosis systems in mechanical systems using historical data and computational intelligence techniques is presented. First, the pre-processing of the data to remove the outliers is performed with the aim of reducing the classification errors. To accomplish this objective, the Density Oriented Fuzzy C-Means (DOFCM) algorithm is used. Later on, the Kernel Fuzzy C-Means (KFCM) algorithm is used to achieve greater separability among the classes, and reducing the classification errors. Finally, an optimization process of the parameters used in the training state by the DOFCM and KFCM for improving the classification results is developed using the bioinspired algorithm Ant Colony Optimization. The proposal was validated using the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) benchmark. The satisfactory results obtained indicate the feasibility of the proposal.

Suggested Citation

  • Adrián Rodríguez Ramos & José M. Bernal de Lázaro & Alberto Prieto-Moreno & Antônio José Silva Neto & Orestes Llanes-Santiago, 2019. "An approach to robust fault diagnosis in mechanical systems using computational intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1601-1615, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1343-1
    DOI: 10.1007/s10845-017-1343-1
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    Citations

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    Cited by:

    1. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    2. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    3. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.

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