Reference:
A. Jamshidi,
S. Faghih-Roohi,
S. Hajizadeh,
A. Núñez,
R. Babuska,
R. Dollevoet,
Z. Li, and
B. De Schutter,
"A big data analysis approach for rail failure risk assessment,"
Risk Analysis, vol. 37, no. 8, pp. 1495-1507, Aug. 2017.
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 paper, the aim is to assess the risk of
a rail failure by analyzing a type of rail surface defects called
squats that are detected automatically among the huge amount 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.