Unsupervised machine learning techniques to prevent faults in railroad switch machines

Nielson Soares, Eduardo Pestana de Aguiar, Amanda Campos Souza and Leonardo Goliatt

International Journal of Critical Infrastructure Protection, 2021, vol. 33, issue C

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.

Keywords: Railroad switch; Computational intelligence; Machine learning; Failure prediction (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1874548221000159
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:33:y:2021:i:c:s1874548221000159

DOI: 10.1016/j.ijcip.2021.100423

Access Statistics for this article

International Journal of Critical Infrastructure Protection is currently edited by Leon Strous

More articles in International Journal of Critical Infrastructure Protection from Elsevier
Bibliographic data for series maintained by Catherine Liu ().