Failures of railway point systems (RPS) often lead to service delays or hazardous situations. A condition monitoring system can be used by railway infrastructure operators to detect the early signs of the deteriorated condition of RPS and prevent failures. This paper presents a methodology for early detection of the changes in the measurements of current drawn by the motor of the point operating equipment (POE) of RPS, which can be used to warn about a possible failure in the system. The proposed methodology uses the One Class Support Vector Machines (OCSVM) classification method with the similarity measure of Edit distance with Real Penalties (ERP). The technique has been developed taking into account specific features of the data of in-field RPS and therefore is able to detect the changes in the measurements of current of the POE with greater accuracy compared to the commonly used threshold-based technique. The data from in-field RPS, which relate to incipient failures of RPS, were used after the deficiencies in the data labelling were removed using expert knowledge. In addition, the possible improvements in the proposed methodology were identified in order for it to be used as an automatic online condition monitoring system.
Switches and crossings (S&C) are critical elements on railway networks. Any failure of S&C usually leads to train delays and cancelations having a negative impact on the quality of service delivered, railway safety and also operating costs. S&C is a multi-component system and to enable proactive prevention of S&C unit faults, with their undesirable consequences, the ability to predict problems at component level is needed. This paper describes the derivation of lifetime distributions of individual S&C components based on field data collected. Maintenance was identified as a potential contributor towards increased frequency of reported S&C faults. The outcomes of the analysis and lifetime distributions for the elements of the switch units provide a means of predicting the expected number of maintenance activities, and associated costs for these units over any specified period of time. They provide an essential input to the optimisation of maintenance in developing an asset management strategy.
A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractManagement of a large portfolio of infrastructure assets is a complex and demanding task for transport agencies. Although extensive research has been conducted on probabilistic models for asset management, in particular bridges, focus has been almost exclusively on deterioration modelling. The model being presented in this study tries to reunite a disjointed system by combining deterioration, inspection and maintenance models. A Petri-Net (PN) modelling approach is employed and the resulting model consists of a number of different modules each with its own source of data, calibration methodology and functionality. The modules interconnect providing a robust framework. The interaction between the modules can be used to provide meaningful outputs useful to railway bridge portfolio managers.
Delivering the railway infrastructure whose functionality is sustainable and uncompromised in terms of safety and availability under ever decreasing budget constraints is a great challenge. The successful accomplishment of this task relies on the effective management of individual assets within a wider whole system perspective. This is a highly complex decision making task where mathematical models are required to enable well informed choices.In this paper a novel modelling framework is proposed for performing the whole system lifecycle cost analysis. The framework is based on two models: railway network performance and costs. Using the former model investigations of the effects of decisions can be carried out for the individual asset and the whole system. A Petri Net modelling technique is used to construct the model which is then analysed by means of Monte Carlo simulations. The infrastructure performance model is then integrated with the cost model to perform the lifecycle cost analysis.A superstructure example is presented to demonstrate the application of the approach. The results show that taking into account interdependencies among the intervention activities greatly influences, not only the performance of the infrastructure, but also its lifecycle costs and thus should be included in the cost analysis. Thus, the proposed models enable more detailed and accurate economic forecast.
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