Rail break and derailment prediction using Probabilistic Graphical Modelling

TL;DR

This paper develops a probabilistic graphical model to predict rail breaks on the South African Iron Ore line, aiming to improve maintenance and safety by estimating risks before derailments occur.

Contribution

It introduces a basic rail break risk prediction model tailored to the Ore Line, incorporating available data and discussing potential extensions with live monitoring systems.

Findings

Model provides risk estimates for rail breaks

Scenario analysis demonstrates model's practical use

Framework sets foundation for real-time risk monitoring

Abstract

Rail breaks are one of the most common causes of derailments internationally. This is no different for the South African Iron Ore line. Many rail breaks occur as a heavy-haul train passes over a crack, large defect or defective weld. In such cases, it is usually too late for the train to slow down in time to prevent a de-railment. Knowing the risk of a rail break occurring associated with a train passing over a section of rail allows for better implementation of maintenance initiatives and mitigating measures. In this paper the Ore Line's specific challenges are discussed and the currently available data that can be used to create a rail break risk prediction model is reviewed. The development of a basic rail break risk prediction model for the Ore Line is then presented. Finally the insight gained from the model is demonstrated by means of discussing various scenarios of various rail break risk. In future work, we are planning on extending this basic model to allow input from live monitoring systems such as the ultrasonic broken rail detection system.