A fault detection method for railway point systems

Vileiniskis, Marius; Remenyte-Prescott, Rasa; Rama, Dovile

Abstract

Failures of railway point systems (RPSs) often lead to service delays or hazardous situations. A condition monitoring system can be used by railway infrastructure operators to detect the early signs of the deteriorated condition of RPSs and thereby prevent failures. This paper presents a methodology for early detection of the changes in the measurement of the current drawn by the motor of the point operating equipment (POE) of an RPS, which can be used to warn about a possible failure in the system. The proposed methodology uses the one-class support vector machine classification method with the similarity measure of edit distance with real penalties. The technique has been developed taking into account specific features of the data of infield RPSs and therefore is able to detect the changes in the measurements of the current of the POE with greater accuracy compared with the commonly used threshold-based technique. The data from infield RPSs, which relate to incipient failures of RPSs, were used after the deficiencies in the data labelling were removed using expert knowledge. In addition, possible improvements in the proposed methodology were identified in order for it to be used as an automatic online condition monitoring system.

Citation

Vileiniskis, M., Remenyte-Prescott, R., & Rama, D. (2016). A fault detection method for railway point systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230(3), https://doi.org/10.1177/0954409714567487

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2014
• Online Publication Date: Feb 9, 2015
• Publication Date: Mar 1, 2016
• Deposit Date: Jul 22, 2016
• Publicly Available Date: Jul 22, 2016
• Journal: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
• Print ISSN: 0954-4097
• Electronic ISSN: 2041-3017
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 230
• Issue: 3
• DOI: https://doi.org/10.1177/0954409714567487
• Keywords: Railway point systems, fault detection, point operating equipment, one-class support vector machine, edit distance with real penalties, fault classification
• Public URL: https://nottingham-repository.worktribe.com/output/978176
• Publisher URL: http://pif.sagepub.com/content/230/3/852.short
• Contract Date: Jul 22, 2016