Prediktiva modeller för degradering av spårgeometri i järnväg

Railways are a vital and effective means of mass transportation and play a vital role in modern transportation and social development. The benefits of the railway compared to other transportation modes are a high capacity, high efficiency and low pollution, and owing to these advantages, railways are nowadays experiencing a higher demand for the transportation of passengers and goods. This is in turn imposing higher demands on the railway capacity and service quality. As a result, infrastructure managers are being driven to develop new strategies and plans to fulfil new requirements, which include a higher level of resilience against failure, a more robust and available infrastructure, and cost reduction. This can be achieved by making efficient and effective maintenance decisions by applying RAMS (reliability, availability, maintainability, and safety) analysis and LCC (life cycle cost) assessment. A major part of the railway maintenance burden is related to track geometry maintenance. Due to the forces induced on the track by traffic, the railway degrades over time, causing deviations from the designed vertical and horizontal alignment. When the track geometry degrades to an unacceptable level, this can cause catastrophic consequences, such as derailment. Maintenance actions are used to control the degradation of the track and restore the geometry condition of the track sections to an acceptable state. With the current advancements in the field of technologies for railway track geometry measurement, a large amount of event data and condition monitoring data is available. Such technologies, along with advances in predictive analytics, are providing the possibility of predicting the track geometry condition in support of a predictive maintenance strategy. The aim of the research conducted for this thesis has been to develop methodologies and tools for the prediction of railway track geometry degradation, in order to facilitate and enhance the capability of making effective decisions for inspection and maintenance planning. To achieve the purpose of this research, literature studies, case studies and simulations have been conducted. Firstly, a literature review was performed to identify the existing knowledge gaps and challenges for track geometry degradation modelling and maintenance planning. Secondly, a case study was conducted to analyse the effect of tamping on the track geometry condition. By considering the track geometry condition before tamping as the predictor, a probabilistic approach was utilised to model the recovery after tamping interventions. Thirdly, a two-level piecewise linear framework was developed to model the track geometry evolution over a spatial and temporal space. This model was implemented in a comprehensive case study. Fourthly, a data-driven analytical model was developed to predict the occurrence of track geometry defects. This model enables infrastructure managers to predict the occurrence of severe isolated geometry defects. Finally, an integrated model was created to investigate the effect of different inspection intervals on the track geometry condition

Simulation of railway track geometry and intelligent maintenance planning : [Simulering av järnvägsspårgeometri och intelligent underhållsplanering]

Track is the fundamental part of railway infrastructure and represents a significant part of maintenance effort and cost. For example, in Sweden, the annual maintenance cost for only track geometry is between110 and 130 MSEK. The quality of the track, mostly, is represented by the track geometry properties. Track geometry degrades with age and usage; and loses its functionality over time. Poor quality of track geometry may result in safety problems, speed reduction, traffic disruption, greater maintenance cost, and higher degradation rate of the other railway components (e.g. rails, wheels, switches, and crossings). Railway track maintenance program development is challenging and requires appropriate modeling which reflects the real-life scenario and integrates influencing factors. In addition, there are several uncertainties in data collection, data analysis, modeling, and the prediction that are needed to be considered. Moreover, there is a lack of integrated platform that is able to access geometry data, extract associatedinformation, and retain this knowledge for supporting adaptive maintenance planning and scheduling. The above challenges necessitate the Infrastructure Manager (IM) to employ a maintenance management system that enables higher capacity for evaluation of track performance, learning from asset history, context-driven awareness, planning & scheduling, and transformation of this information to knowledge for decision making. The SIMTRACK project will facilitate simulation-based platform that enables development of a tools,methodologies and techniques for optimization of track geometry maintenance planning, scheduling andopportunistic maintenance. This will provide a basis to predict track geometry degradation, analyse therisk 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. The project structure track is structured into 6 work packages. WP1 deals with the project management. WP2 presentsthe industrial scenarios, specifications and requirements that provide inputs to WP3 and WP4. WP3 andWP4 are defined as predictive modelling and analytics of track geometry condition and trackmaintenance optimization and decision support system respectively. WP5 is dedicated to evaluation ofabsolute track geometry condition. Finally, WP6 deals with dissemination and exploitation, is devotedfor formulating comprehensive plans for results assimilation by the partners and set the ground for theexploitation. Figure 1 shows the work packages and their relationships.Simtrac

Investigation of the effect of the inspection intervals on the track geometry condition

In order to evaluate the railway track geometry condition and plan maintenance activities, track inspection cars run over the track at specific times to monitor it and record geometry measurements. Applying an adequate inspection interval is vital to ensure the availability, safety and quality of the railway track, at the lowest possible cost. The aim of this study has been to investigate the effect of different inspection intervals on the track geometry condition. To achieve this, an integrated statistical model was developed to predict the track geometry condition given different inspection intervals. In order to model the evolution of the track geometry condition, a piecewise exponential model was used which considers break points at the maintenance times. Ordinal logistic regression was applied to model the probability of the occurrence of severe isolated defects. The Monte Carlo technique was used to simulate the track geometry behaviour given different inspection intervals. The results of the proposed model support the decision-making process regarding the selection of the most adequate inspection interval. The applicability of the model was tested in a case study on the Main Western Line in Sweden.Validerad;2020;Nivå 2;2020-06-09 (alebob)</p

Prediction of railway track geometry defects : a case study

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within the study, a defect-based model has been proposed to identify the degradation pattern of isolated longitudinal level defects. The proposed model considered the occurrence of shock events in the degradation path. Furthermore, the effectiveness of tamping intervention in rectifying the longitudinal level defects was analysed. The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects. In addition, a section-based model has been developed using binary logistic regression to predict the probability of occurrence of isolated defects associated with track sections. The model considered the standard deviation and kurtosis of longitudinal level as explanatory variables. It has been found that the kurtosis of the longitudinal level is a statistically significant predictor of the occurrence of isolated longitudinal level defects in a given track section. The validation results show that the proposed binary logistic regression model can be used to predict the occurrence of isolated defects in a track section.Validerad;2020;Nivå 2;2020-06-03 (alebob)</p

Allocation of effective maintenance limit for railway track geometry

The objective of this study has been to develop an approach to the allocation of an effective maintenancelimit for track geometry maintenance that leads to a minimisation of the total annual maintenancecost. A cost model was developed by considering the cost associated with inspection, preventivemaintenance, normal corrective maintenance and emergency corrective maintenance. The standarddeviation and extreme values of isolated defects of the longitudinal level were used as quality indicatorsfor preventive and corrective maintenance activities. The Monte Carlo technique was used tosimulate the track geometry behaviour under different maintenance limit scenarios and the effectivelimit was determined which minimises the total maintenance cost. The applicability of the model wastested in a case study on the Main Western Line in Sweden. Finally, a sensitivity analysis was carriedout on the inspection intervals, the emergency corrective maintenance cost and the maintenanceresponse 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 suggestedthat the lower bound of the optimal region should be selected.Validerad;2019;Nivå 2;2019-12-06 (johcin)</p

Inspection Optimization under imperfect maintenance performance

Scheduled maintenance and inspection development is one of the main requirements for emergency equipment and safety devices. These types of devices have hidden functions which are used intermittently or infrequently, so their failure will not be evident to the operating crew. The analytical model presented in this paper deals with the periodically tested units with overhauls (preventive maintenance) after certain number of inspections and a renewal after a series of overhauls. The cost based optimization method presented in this paper identifies the optimum interval and frequency of Failure Finding Inspection (FFI) and restoration. In the proposed model, repair due to failures found by inspection makes the unit As Bad As Old, and restoration/overhaul action rejuvenates the unit to any condition between As Good As New and As Bad As Old. As Good As New effectiveness also is considered for renewal action. It considers inspection and repair times, and takes into account the costs associated with inspection, repair, restoration, and also the cost of accidents due to the occurrence of multiple failure. The results show that when the unit is not under aging process, the optimal alternative for each inspection interval is the one with highest possible number of inspection without restoration. Finally, it is observed that when the cost of accident is quite high it is needed to perform inspections at smaller intervals to control the risk of accident

Application of principal component analysis and artificial neural network in prediction of track geometry degradation

Railway track geometry subjects to degradation with age and usage and highly affects track functionality. An accurate prediction of track geometry degradation is essential to enhance the performance of maintenance planning and scheduling. In this study, artificial neural network is used to predict track geometry degradation rate. The data of five lines from Swedish railway network are used to develop the model. The standard deviation of the longitudinal level is used as quality indicator for track geometry degradation. For all track sections, a set of features which may contribute in track geometry degradation are collected. Principal component analysis method is used to reduce the dimension of the features to facilitate the training of the ANN model. Then, the new principal features considered as inputs to ANN model. The results indicate that the proposed method can be used to predict track geometry degradation along the track line.Finansiär: Simtrack projectISBN för värdpublikation: 978-91-7790-475-5</p

Optimal opportunistic tamping scheduling for railway track geometry

This study has been dedicated to the optimization of opportunistic tamping scheduling. The aim ofthis study has been to schedule tamping activities in such a way that the total maintenance costs andthe number of unplanned tamping activities are minimized. To achieve this, the track geometry tampingscheduling problem was defined and formulated as a mixed integer linear programming (MILP)model and a genetic algorithm was used to solve the problem. Both the standard deviation of thelongitudinal level and the extreme values of isolated defects were used to characterize the trackgeometry quality and to plan maintenance activities. The performance of the proposed model wastested on data collected from the Main Western Line in Sweden. The results show that different scenariosfor controlling and managing isolated defects will result in optimal scheduling plan. It is alsofound that to achieve more realistic results, the speed of the tamping machine and the unused life ofthe track sections should be considered in the model. Moreover, the results show that prediction ofgeometry condition without considering the destructive effect of tamping will lead to an underestimationof the maintenance needs by 2%.Validerad;2021;Nivå 2;2021-09-29 (alebob);Funder: Bana Väg För Framtiden (BVFF) </p