Abstract
Maintaining railway tracks is capital intensive, time consuming and safety-critical. Novel maintenance methods can decrease these costs and improve efficiency by analysis of collected data from tracks. Twist is one of the common track failures, which poses the risk of derailment, fatalities, injuries and financial loss. In this paper, track parameters are studied for part of a major railway route in Iran. Polynomial regression and association rules, which are popular data mining approaches are used to discover relationship between twist failure with failures of other track parameters for the period between 2018 and 2020. The results show that alignment and super elevation have the highest impact on twist and most of the times these failures occur simultaneously. By adopting this approach twist failure can be identified in order to avoid chain failure and move toward condition-based maintenance.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call