Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Abstract

Performance evaluation and maintenance planning are gaining importance with ageing rail infrastructure and increasing demand on track safety and continuous availability. The discrete/point railway assets (e.g. bridges, level crossings) together with extended track sections constitute the main railway network infrastructure. The former has important implications in train safety, riding comfort and operating expenditures due to local intensified degradation and plays a role in effective network capacity due to their large quantity. The heterogeneity in asset features and operating environment also adds difficulties to efficient maintenance planning of multiple discrete assets. The current review screens the issue to level crossings, as little concern has been engaged to this asset type, and draws together different perspectives related to their maintenance management. The systems thinking approach is integrated and two levels of asset management (i.e. micro-and macro-level) are used to structure the synthesis, which are interdependent and synergistic. Two major approaches, namely, the mechanistic and data-driven modelling are synthesised. Both contribute to the maintenance knowledge and their comparisons are elaborated. Limitations in existing studies are identified and directions for future research are provided, aiming to contribute to a more refined 'inspection and diagnosis' process to properly capture the local track issues and move towards system-level maintenance approach for multiple level crossings.

Acceleration-based condition monitoring of track longitudinal level using multiple regression models

“…Two models are built, to predict both the RMS and the MAX longitudinal level considering the RMS bogie vertical acceleration as input. In the implementation foreseen in, 19 the track condition monitoring system takes advantage of the dataset from the TRV only in the training phase of the model, thus a dashed line is adopted in Figure 1.…”

“…In this framework, different machine learning approaches have been proposed in recent years. 19 Probabilistic models based on Markov chains 20 and classification algorithms (Support Vector Machine, decision tree, augmented Bayesian) 21,22 were used to predict the evolution of synthetic indexes related to track geometry.…”

The role of multi-fidelity modelling in adaptation and recovery of engineering systems

“…Most approaches for adaptation and response to natural or man-made disasters lay their foundation on the premise that under a disruption an optimal adaptation decision making scheme can be found that will maintain the functionality of a system at certain minimum operational level. This can be achieved through intervention, by adapting the system operational variables, or other alternative system approaches [1]. In all cases, some form of technique needs to inform decision making, and current trends of research indicate that there is a growing interest in the application of high-fidelity (HF) modelling as a tool to inform decisions.…”