Abstract
Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.
Highlights
Among all transportation infrastructure, the railway network is one of the most successful transport systems for reducing transportation cost, traffic congestion, and air pollution emission levels
Risk is intuitively connected to decision making under uncertainty.[4]. Recent developments in big data analytic for uncertainty management and risk assessment of industrial systems have been studied by Wu and Birge[5] and Choi et al[6] Risk assessment of large-scale systems is of current interest across many application domains such as healthcare,(7) environmental safety,(8,9) transportation,(10–13) business,(14) and product development.[15]. In particular for railway applications, risk assessment is critical for the prediction of infrastructure health condition within a
For a detected squat with measured visual lengths in one million gross tons (MGT) step, we estimate the risk of rail failure as follows
Summary
The aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. We perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach
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