Lulea Railway Research Center

Luleå, Sweden

Lulea Railway Research Center

Luleå, Sweden
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Lin J.,Lulea Railway Research Center | Lin J.,Lulea University of Technology | Pulido J.,ReliaSoft Corporation | Asplund M.,Lulea Railway Research Center | Asplund M.,Lulea University of Technology
Proceedings - Annual Reliability and Maintainability Symposium | Year: 2014

This paper undertakes a reliability study using both classical and Bayesian semi-parametric frameworks to explore the impact of a locomotive wheel's position on its service lifetime and to predict its other reliability characteristics. The goal is to illustrate how degradation data can be modeled and analyzed by using classical and Bayesian approaches. The adopted data in the case study have been collected from the Swedish company. The results show that: 1) an exponential degradation path is a better choice for the studied locomotive wheels; 2) both classical and Bayesian semi-parametric approaches are useful tools to analysis degradation data; 3) under given operation conditions, the position of the locomotive wheel could influence its reliability. © 2014 IEEE.


Lin J.,Lulea University of Technology | Lin J.,Lulea Railway Research Center | Pulido J.,ReliaSoft Corporation | Asplund M.,Lulea University of Technology
Reliability Engineering and System Safety | Year: 2015

This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both accelerated life tests (ALT) and design of experiments (DOE) technology are used to determine how each critical factor affects the prediction of performance. With the Bayesian semi-parametric approach, a piecewise constant hazard regression model is used to establish the lifetime using degradation data. Gamma frailties are included to explore the influence of unobserved covariates within the same group. Ideally, results from the classical and Bayesian approaches will complement each other. To demonstrate these approaches, this paper considers a case study of locomotive wheel-set reliability. The degradation data are prepared by considering an Exponential and a Power degradation path separately. The results show that both classical and Bayesian semi-parametric approaches are useful tools to analyse degradation data and can, therefore, support a company in decision making for preventive maintenance. The approach can be applied to other technical problems (e.g. other industries, other components). © 2014 Elsevier Ltd. All rights reserved.


Famurewa S.M.,Lulea University of Technology | Famurewa S.M.,Lulea Railway Research Center | Juntti U.,Lulea Railway Research Center | Juntti U.,Performance in Cold AB | And 3 more authors.
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit | Year: 2015

The demand for increased capacity on existing railway networks is a challenge for many Europe-based infrastructure managers; addressing this challenge requires augmented utilisation of track possession time. It is considered that large-scale maintenance tasks such as geometry maintenance can be improved; thus, reducing the on-track maintenance time and allowing more traffic. In this study, an analysis of track geometry maintenance was performed with the objective of reducing the required possession time. The procedure and models for planning and optimizing track geometry maintenance are presented. A statistical model that uses a simulation approach was used to determine the condition of the track geometry, and a schedule optimization problem was formulated to support intervention decisions and optimize the track possession time. The results of the case study show that optimizing the maintenance shift length and cycle length are opportunities to reduce the extent of track possession required for the maintenance of the track geometry. In addition, continuous improvement of the tamping process through lean analysis promises about a 45% reduction in the required possession time for a tamping cycle. © 2015 Institution of Mechanical Engineers.


Famurewa S.M.,Lulea University of Technology | Famurewa S.M.,Lulea Railway Research Center | Parida A.,Lulea University of Technology | Parida A.,Lulea Railway Research Center | And 2 more authors.
International Journal of COMADEM | Year: 2015

Railway transport infrastructure is a linearly distributed asset that requires an effective performance management system to meet sectional and overall business objectives. In particular, an effective performance measurement system with relevant analysis technique in an ongoing manner is necessary to facilitate continuous improvement. Maintenance performance measurement (MPM) is essential to quantify the impact of past maintenance decisions and actions and also to support new decisions. This article presents the challenges of implementing and using MPM systems for maintenance decisions in the railway industry. Thereafter, a risk matrix with maintenance performance indicators is introduced as a complementary analysis tool to identify weak links on a railway line. A case study of a section on the heavy haul line of the Swedish Transport Administration railway network is presented to demonstrate the application of the risk matrix tool for continuous improvement. The results identified the bottlenecks on the line section, which are improvement opportunities for maintenance performance in terms of service quality and capacity target of the infrastructure manager.


Xin T.,Beijing Jiaotong University | Famurewa S.M.,Lulea University of Technology | Famurewa S.M.,Lulea Railway Research Center | Gao L.,Beijing Jiaotong University | And 4 more authors.
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit | Year: 2016

The quality of track geometry is an important aspect in railway engineering, as it reflects any deviations and thus the actual condition of a track. Monitoring and prediction of a relevant geometry quality parameter provides an opportunity for effective maintenance, thus creating the advantages of extending the life of the asset, reducing maintenance costs and minimizing possession time requirements. Effective maintenance practice requires a good understanding of the behaviour of track structures over time and also prediction of its condition using only a few inputs. This paper presents a grey-system-theory-based model for predicting track irregularity. Three variants of the grey model are presented and their performances are compared with simple linear and exponential models. Regression models and the grey-system-theory-based models are used to obtain the standard deviation of the longitudinal level from a series of geometry inspection data. The overall performances of the models are evaluated in terms of the regression and prediction accuracies, and it is shown that a Fourier series modification of the grey model has the best performance and the minimum error. The contribution of this paper is the creation of a prediction model for track geometry quality, which is essential for planning and scheduling of preventive geometry maintenance. © Institution of Mechanical Engineers.


Lin J.,Lulea University of Technology | Lin J.,Lulea Railway Research Center
Journal of Quality and Reliability Engineering | Year: 2014

The recent proliferation of Markov chain Monte Carlo (MCMC) approaches has led to the use of the Bayesian inference in a wide variety of fields. To facilitate MCMC applications, this paper proposes an integrated procedure for Bayesian inference using MCMC methods, from a reliability perspective. The goal is to build a framework for related academic research and engineering applications to implement modern computational-based Bayesian approaches, especially for reliability inferences. The procedure developed here is a continuous improvement process with four stages (Plan, Do, Study, and Action) and 11 steps, including: (1) data preparation; (2) prior inspection and integration; (3) prior selection; (4) model selection; (5) posterior sampling; (6) MCMC convergence diagnostic; (7) Monte Carlo error diagnostic; (8) model improvement; (9) model comparison; (10) inference making; (11) data updating and inference improvement. The paper illustrates the proposed procedure using a case study. © 2014 Jing Lin.


Khouy I.A.K.,Lulea University of Technology | Schunnesson H.,Lulea University of Technology | Nissen A.,Lulea Railway Research Center | Juntti U.J.,Lulea University of Technology
International Journal of COMADEM | Year: 2012

The measurement and improvement of track quality are key issues in determining both the time and cost of railway maintenance. Efficient track geometry maintenance ensures optimum allocation of limited maintenance resources and has an enormous effect on maintenance efficiency. Applying the appropriate tamping strategy also helps reduce maintenance costs, making operations more cost effective and leading to increased safety and passenger comfort. In this paper, track geometry data from the iron ore line in northern Sweden, which handles both passenger and freight trains, are used to calculate track quality degradation trend in a cold climate. The paper describes Trafik verket's (Swedish Transport Administration) tamping strategy and illustrates the distribution of safety failures in different seasons. It also analyses the track geometry degradation and discuss about the possible reasons for distribution of failures over a year and along the track. © 2012 COMADEM International.


Lin J.,Lulea University of Technology | Lin J.,Lulea Railway Research Center | Asplund M.,Lulea University of Technology | Asplund M.,Lulea Railway Research Center
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit | Year: 2013

A reliability study based on a Bayesian semi-parametric framework is performed in order to explore the impact of the position of a locomotive wheel on its service lifetime and to predict its other reliability characteristics. A piecewise constant hazard regression model is used to analyse the lifetime of locomotive wheels using degradation data and taking into account the bogie on which the wheel is located. Gamma frailties are included in this study to explore unobserved covariates within the same group. The goal is to flexibly determine reliability for the wheel. A case study is performed using Markov chain Monte Carlo methods and the following conclusions are drawn. First, a polynomial degradation path is a better choice for the studied locomotive wheels; second, under given operational conditions, the position of the locomotive wheel, i.e. on which bogie it is mounted, can influence its reliability; third, a piecewise constant hazard regression model can be used to undertake reliability studies; fourth, considering gamma frailties is useful for exploring the influence of unobserved covariates; and fifth, the wheels have a higher failure risk after running a threshold distance, a finding which could be applied in optimisation of maintenance activities. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.


Famurewa S.M.,Lulea University of Technology | Asplund M.,Lulea University of Technology | Rantatalo M.,Lulea University of Technology | Rantatalo M.,Lulea Railway Research Center | And 3 more authors.
Structure and Infrastructure Engineering | Year: 2015

Railway transport system is massive and complex, and as such it requires effective maintenance to achieve the business goal of safe, economic and sustainable transportation of passengers and goods. The growing demand for improved service quality and capacity target by railway infrastructure managers requires appropriate maintenance analysis to facilitate continuous improvement of infrastructure performance. This paper presents the application of risk matrix as a maintenance analysis method for the identification of track zones that are bottlenecks that limit operational capacity and quality. Furthermore, an adapted criticality analysis method is proposed to create a hierarchical improvement list for addressing the problem of train mission interruption and reduced operational capacity. A case study of a line section of the Swedish network is presented. The result classifies the zones on the line section into different risk categories based on their contribution to loss of capacity and punctuality. In addition, an improvement list for the lower-level system is presented to facilitate maintenance decisions and continuous improvement at both operational and strategic levels. © 2015, © 2015 Taylor & Francis.


Famurewa S.M.,Lulea University of Technology | Xin T.,Lulea University of Technology | Xin T.,Beijing Jiaotong University | Rantatalo M.,Lulea University of Technology | And 3 more authors.
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit | Year: 2015

Optimum allocation and efficient utilisation of track possession time are becoming important topics in railway infrastructure management due to increasing capacity demands. This development and other requirements of modern infrastructure management necessitate the improvement of planning and scheduling of large-scale maintenance activities such as tamping. It is therefore necessary to develop short-, medium- and long-term plans for performing tamping on a network or track section within a definite time horizon. To this end, two key aspects of infrastructure maintenance planning are considered in this paper, deterioration modelling and scheduling optimisation. An exponential deterioration function is applied to model the geometry quality of a series of 200m segments of a 130km line section, and an empirical model for recovery after tamping intervention is developed. These two models are subsequently used to generate a methodology to optimise a schedule for tamping intervention by minimising the total cost of intervention including the cost of track possession while geometry quality is ascertained to be within a desirable limit. The modelling considers two types of tamping interventions, preventive and corrective, with different intervention limits and tamping machines. The result of this paper suggests a tamping plan which will lead to optimum allocation of track possession time while maintaining the track geometry quality within specified limits. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

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