Railroad Track Geometric Degradation Analysis: A BNSF Case Study

Modeling Expected Failure Considering Repair Time and Degradation: A Rail System Case Study

“…Scholars study a railroad track degradation analysis for three different geometry failure modes. Effective degradation factors are found using the BNSF data set, and inspection intervals are researched to lessen the impact of hidden maintenance actions [16]. A numerical method is introduced for estimating the Expected Number of Failures (ENF) and Cumulative Intensity Function (CIF).…”

“…The data set from BNFS 2007 to 2013 is used in this study. In the data set, a track can fail geometrically in one of three ways as follows [16]:  The first is cross level failure mode, which assesses the variation in top surface elevation between two rails at any particular location along the railroad track. Since the rails can move up or down when under load, the cross-level Journal of Transportation Technologies measurement is typically done while they are in motion (Figure 7).…”

Contribution to Railway Track Maintenance Planning from the Analysis of Dynamic Movements of Trains

“…An example of an administrative model based on this concept is EN 13848-5 (2008) [20], which establishes three possible measures based on the severity of geometric deviations: the alert limit (AL), which, if reached, requires an analysis of the track geometry to define a future intervention plan, usually to be carried out in a horizon of up to one year; the intervention limit (IL), which, if reached, requires corrective maintenance; and the immediate alert limit (IAL), which, when reached, imposes a speed reduction or track closure for immediate maintenance [21]. As another example of condition-based planning models, several railroads have developed methods for calculating track quality indices (TQI) using inspection data [22]. Such indexes are based on specific formulations that consider, e.g., the standard deviation of geometric parameter readings or the severity of isolated defects to define reference values that represent the track's condition in a summarized way, serving as a reference to indicate the need for maintenance (for examples of these models, see Soleimanmeigouni et al (2016) [1] and Litherland & Andrews (2019) [23]).…”

“…Therefore, as an effort to improve such traditional procedures, management methods have been developed aiming to carry out the so-called predictive maintenance, in which the maintenance is strategically defined so that interventions are not anticipated or postponed, but performed at the optimal time to guarantee a maximum use of the potential life of the track, without tolerating a level of degradation that could compromise safety and performance [25,26]. Due to the relevance of such predictive maintenance methods, the literature has a significantly large number of works devoted to their development, as can be seen in Sharma et al (2018) [18], Bakhtiary et al (2020) [19], Soleimanmeigouni et al (2020) [21], Rahimikelarijani et al (2020) [22], Andrews et al (2014) [27], Wen et al (2016) [28], Lee et al (2017) [29], Khouzani et al (2017) [30], Khajehei et al (2019) [31], Nielsen et al (2018) [32], Andrade & Teixeira (2015) [33], Su et al (2019) [34], Sadeghi et al (2017) [35], Neuhold et al (2020) [36], and Yang et al (2020) [37]. However, despite the volume and variety of approaches, the most widely used strategy to reduce geometric maintenance costs, in the planning methods seen in such referred works, consists, in short, in establishing a system capable of predicting the track's behavior over time, so that the moment when the geometric deviations will reach the safety tolerances is identified in advance.…”