Quantifying the suitability and feasibility of predictive maintenance approaches

Predictive maintenance is a promising concept for maintenance optimization which requires reliable and accurate predictions of a system's lifetime. Despite the many studies on this topic, selecting the best approach is still a topic of debate. In general, predictive maintenance approaches can be roughly classified as knowledge-based, data analytics and physics-based models. However, this classification does not provide maintainers clear guidance on how to select the most suitable approach for a specific case. This work, therefore, presents a selection method for this process. For that purpose, a list of selection criteria was established, and six predictive maintenance approaches were analysed. The proposed selection process is based on two main groups of criteria: the suitability criteria check the match with the desired ambition level of predictive maintenance, while the feasibility criteria identify whether this can be realized, given the labour, models and data available. Finally, three case studies are presented, demonstrating that the tool effectively guides to an optimal approach.</p

Uncertainty propagation in rail wear prediction using an analytical method and field observations

Rail wear management based on accurate rail wear prediction is essential for railway maintenance. The implementation of rail wear prediction models in maintenance decision tools is not yet available due to detailed modelling and the absence of direct coupling with operational conditions. A method that does not provide any confidence interval on the prediction is not very helpful if one wants to use the results of the prediction for maintenance decision-making and there is variation in the input. Therefore, in this study a wear prediction model that does take into account these limitations is used to predict the amount of rail wear with certain confidence bounds. The uncertainty in the output of the model is quantified. This is realized by considering probability distribution functions for the input parameters and analytical analyses. The results obtained from these analyses are then compared with field measurements and a good agreement is found

Towards the development of a hybrid methodology of head checks in railway infrastructure

In this paper, the first step towards the development of a hybrid methodology for the monitoring of head checks is discussed. The proposed hybrid method combines a data driven approach with physical modelling of the rail in order to obtain an early stage warning for head checks. Rail defect detection at an early stage of the growth can be challenging and the existence of the seed defects can be confused with non-defect objects on the rail. Thus, a physical model is proposed to investigate how head checks, in particular in curved tracks, initiate and evolve. Track characteristics and loading, e.g. track geometry and track tonnage, are considered to analyze crack initiation by using the Whole Life Rail Model (WLRM) for Rolling Contact Fatigue (RCF) relying on meta-models. The results of the physical modelling and the rail defect observations obtained from the data analysis on the eddy current (EC) measurements are then compared. The physics based model only suggests whether a crack will be initiated or not, it does not give information about the size of the crack. Hence, the next step is to develop an evolution model from the EC and Ultrasonic (US) measurements data, from which the crack size can be determined. This combination of physics based and data-driven evolution model is thus regarded as the hybrid method. This hybrid method can be a robust tool for the prediction of rail condition, as it eases the visualization of the rail degradation and keeps infrastructure managers informed of the actual rail condition that can be confirmed with rail inspections. Finally, real-life measurements from a track in the Dutch railway network are used to show the (potential) benefits of the proposed methodology