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Unsupervised machine learning techniques to prevent faults in railroad switch machines

Author

Listed:
  • Soares, Nielson
  • Aguiar, Eduardo Pestana de
  • Souza, Amanda Campos
  • Goliatt, Leonardo

Abstract

Railroad switch machines are essential electromechanical equipment in a railway network, and the occurrence of failures in such equipment can cause railroad interruptions and lead to potential economic losses. Thus, early diagnosis of these failures can represent a reduction in costs and an increase in productivity. This paper aims to propose a predictive model based on computational intelligence techniques, to solve this problem. The applied methodology includes feature extraction and selection procedures based on hypothesis tests and unsupervised machine learning models. The proposed model was tested in a database made available by a Brazilian railway company and proved to be efficient once it has considered critical operations conducted in the vicinity of the ones classified as faults.

Suggested Citation

  • Soares, Nielson & Aguiar, Eduardo Pestana de & Souza, Amanda Campos & Goliatt, Leonardo, 2021. "Unsupervised machine learning techniques to prevent faults in railroad switch machines," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).
    • Handle: RePEc:eee:ijocip:v:33:y:2021:i:c:s1874548221000159
    DOI: 10.1016/j.ijcip.2021.100423
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    References listed on IDEAS

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    1. Tudor Barbu, 2013. "Variational Image Denoising Approach with Diffusion Porous Media Flow," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-8, January.
    2. García, Fausto P. & Pedregal, Diego J. & Roberts, Clive, 2010. "Time series methods applied to failure prediction and detection," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 698-703.
    3. García Márquez, Fausto Pedro & Schmid, Felix, 2007. "A digital filter-based approach to the remote condition monitoring of railway turnouts," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 830-840.
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