Allocation of effective maintenance limit for railway track geometry

Abstract

The objective of this study has been to develop an approach to the allocation of an effective maintenance limit for track geometry maintenance that leads to a minimisation of the total annual maintenance cost. A cost model was developed by considering the cost associated with inspection, preventive maintenance, normal corrective maintenance and emergency corrective maintenance. The standard deviation and extreme values of isolated defects of the longitudinal level were used as quality indicators for preventive and corrective maintenance activities. The Monte Carlo technique was used to simulate the track geometry behaviour under different maintenance limit scenarios and the effective limit was determined which minimises the total maintenance cost. The applicability of the model was tested in a case study on the Main Western Line in Sweden. Finally, a sensitivity analysis was carried out on the inspection intervals, the emergency corrective maintenance cost and the maintenance response time. The results show that there is an optimal region for selecting an effective limit. However, by considering the safety aspects in track geometry maintenance planning, it is suggested that the lower bound of the optimal region should be selected.

Optimal opportunistic tamping scheduling for railway track geometry

“…There exist a wide variety of models in the literature that have been used to model the track geometry degradation between two maintenance cycles and on the basis of recorded measurement data over time or in relation to usage. These models include the linear model (Caetano & Teixeira, 2015;Caetano & Teixeira, 2016;Khajehei et al, 2019;Lee et al, 2018), the exponential model (Peralta et al, 2018;Quiroga & Schnieder, 2012), the grey model (Chaolong, Weixiang, Futian, & Hanning, 2012;Xin, Famurewa, Gao, Kumar, & Zhang, 2016), and the Wiener process (Soleimanmeigouni, Ahmadi, Letot, Nissen, & Kumar, 2016), among other models. In the present study, the exponential model presented in equation (2) has been used to model the track geometry degradation between two maintenance cycles.…”

“…The cost parameters and initial model parameters, presented in Tables 2 and 3, were set based on discussions with Trafikverket and Infranord maintenance experts. Regarding the cost associated with the occurrence of UH2 defects, it was assumed that the occurrence of UH2 defects would not lead to derailment and the cost was set based on Khajehei et al (2019). In addition, the parameters of the GA were set according to Table 4.…”

“…Isolated defects significantly influence the running behaviour of trains and are the main reason for conducting corrective tamping activities (Khajehei, Ahmadi, Soleimanmeigouni, & Nissen, 2019). Therefore, both these indicators should be considered in geometry evaluation to have a better picture of the track geometry condition and to draw up an effective and efficient tamping schedule.…”

Optimal track geometry maintenance limits using machine learning: A case study

“…These parameters may result in degradation and failure of railway assets. 1,2 Whenever the quality of track geometry reaches a certain maintenance limit, a proper maintenance action needs to be taken into account to maintain the quality of track in an acceptable level. Maintenance actions like manual intervention, tamping and stone-blowing can be employed to restore the quality of the track geometry to a better condition.…”

“…Determining the optimal track geometry maintenance limit had been the main concern of a number of studies in the recent years. Khajehei et al 1 proposed a framework based on which the effective maintenance limit can be determined. These authors consider standard deviation of longitudinal level and the extreme value of isolated defects of longitudinal level as track quality indices.…”