Researcher: Alireza Ahmadi (PL), Arne Nissen (PL Trafikverket), Adithya Thaduri, Iman Soleimanmeigouni, Hamid Khajei
Sponsor: BVFF, JVTC and Infranord
Goal: The SIMTRACK project will facilitate a simulation-based platform that enables development of tools, methodologies and techniques for optimization of track geometry maintenance planning, scheduling and opportunistic maintenance. This will provide a basis to predict track geometry degradation, analyse the risk of failures and forecast the maintenance activities as well as renewal investment requirements. The results will enhance safety, maximize capacity utilization, and lead to an efficient and cost effective maintenance program.
Project Status and Results: Generally, the datasets recorded in different inspection runs suffer from positional errors. Since the aim of the predictive models in this project is to predict the occurrence of specific-location defects, it is of great importance to reduce positional errors before developing the models. This is done by applying an alignment method which is a combination of COW and RAFFT algorithms. It is concluded that the developed combined alignment method is flexible to address both constant and non-constant shifts between two datasets which keeping the original shape of measurements.
After performing data alignment, a main step towards prediction of track geometry condition is to predict the occurrence of isolated defects. Within the project two models, i.e. defect-based and section-based are developed to predict the occurrence of isolated defects. The aim of defect based mode is to model the changes in defect’s amplitudes over time, see figure 1. It is found that isolated defects of twist and longitudinal level have a linear pattern over time. In addition, in this project, we take advantage of the first and second order derivatives of original defects to identify the severity of isolated defects. The aim of the section-based model is to predict the occurrence of isolated defects by considering the aggregated quality indices as explanatory variable. The results show that there is a significant relation between the value of standard deviation and kurtosis of geometry parameter and occurrence of isolated defects, see figure 2. This information can be used to plan maintenance activities by considering the probability of occurrence of isolated defects in different track sections.
In order to model the evolution of track geometry condition, a two-level piecewise linear model was developed. The model can be used to simulate track geometry degradation for multiple tamping cycles and track sections.
In order to predict the track geometry degradation rate in different locations of track, Artificial Neural Network (ANN) model is applied. The output of the ANN model not only will provide a more accurate prediction of degradation rate, but also support decision making process by identifying those factors which have a significant effect on degradation rate. The proposed predictive models within the project are used to simulate long-term track geometry behaviour considering different maintenance and inspection scenarios.
One of the key steps in defining maintenance strategy is allocation of a proper maintenance limit to trigger preventive maintenance actions. The objective is to find a planning limit so that the total cost of maintenance would be minimized while keeping geometry condition in the acceptable level. Using the developed simulation framework the expected cost of different planning limits will be estimated and compared to find the most efficient one,
In addition to setting the planning limit, applying an adequate inspection interval is vital to ensure the availability, safety and quality of the railway track, at the lowest possible cost. Therefore, another simulation framework was developed within the project to investigate the effect of different inspection intervals on the track geometry condition, see figure 3.Based on the results obtained, it was observed that varying the length of the inspection intervals has a significant effect on the percentage of time spent by the track in different longitudinal level states. It must be noted that the proposed framework can also be used to predict the number of different maintenance actions.
The final aim of the decision support system is to propose an opportunistic scheduling plan that minimize the total maintenance costs while keep track geometry condition in an acceptable level. To achieve this, the track geometry tamping scheduling problem was defined and formulated as a mixed integer linear programming (MILP) model and a genetic algorithm was used to solve the problem. The figure 4 shows an example of optimal tamping scheduling considering the opportunistic maintenance concept.
Some of the results obtained are as follow: 1) different scenarios for controlling and managing isolated defects will result in optimal scheduling plan 2) to achieve more realistic results, the speed of the tamping machine and the unused life of the track sections should be considered in the model 3) ignoring the destructive effect of tamping in prediction of geometry condition will cause an underestimation of the maintenance needs by about 2%.